Categories

• Algorithms
  • Active Learning
  • Adaptive Data Analysis
  • Adversarial Learning
  • AutoML
  • Bandit Algorithms
  • Boosting and Ensemble Methods
  • Classification
  • Clustering
  • Collaborative Filtering
  • Components Analysis (e.g., CCA, ICA, LDA, PCA)
  • Density Estimation
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  • Few-Shot Learning
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Algorithms

Active Learning  [Top]

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning	Andreas Kirsch · Joost van Amersfoort · Yarin Gal
Bayesian Batch Active Learning as Sparse Subset Approximation	Robert Pinsler · Jonathan Gordon · Eric Nalisnick · José Miguel Hernández-Lobato
Cost Effective Active Search	Shali Jiang · Roman Garnett · Benjamin Moseley
Exact sampling of determinantal point processes with sublinear time preprocessing	Michal Derezinski · Daniele Calandriello · Michal Valko
Flattening a Hierarchical Clustering through Active Learning	Fabio Vitale · Anand Rajagopalan · Claudio Gentile
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning	Weishi Shi · Qi Yu
Learning Nearest Neighbor Graphs from Noisy Distance Samples	Blake Mason · Ardhendu Tripathy · Robert Nowak
Machine Teaching of Active Sequential Learners	Tomi Peltola · Mustafa Mert Çelikok · Pedram Daee · Samuel Kaski
The Label Complexity of Active Learning from Observational Data	Songbai Yan · Kamalika Chaudhuri · Tara Javidi

Adaptive Data Analysis  [Top]

A Meta-Analysis of Overfitting in Machine Learning	Rebecca Roelofs · Vaishaal Shankar · Benjamin Recht · Sara Fridovich-Keil · Moritz Hardt · John Miller · Ludwig Schmidt
A Necessary and Sufficient Stability Notion for Adaptive Generalization	Moshe Shenfeld · Katrina Ligett
Model Similarity Mitigates Test Set Overuse	Horia Mania · John Miller · Ludwig Schmidt · Moritz Hardt · Benjamin Recht
Optimal Sampling and Clustering in the Stochastic Block Model	Se-Young Yun · Alexandre Proutiere

Adversarial Learning  [Top]

A Game Theoretic Approach to Class-wise Selective Rationalization	Shiyu Chang · Yang Zhang · Mo Yu · Tommi Jaakkola
A Little Is Enough: Circumventing Defenses For Distributed Learning	Moran Baruch · Gilad Baruch · Yoav Goldberg
A New Defense Against Adversarial Images: Turning a Weakness into a Strength	Shengyuan Hu · Tao Yu · Chuan Guo · Wei-Lun Chao · Kilian Weinberger
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers	Guang-He Lee · Yang Yuan · Shiyu Chang · Tommi Jaakkola
Adversarial training for free!	Ali Shafahi · Mahyar Najibi · Mohammad Amin Ghiasi · Zheng Xu · John Dickerson · Christoph Studer · Larry Davis · Gavin Taylor · Tom Goldstein
Certifiable Robustness to Graph Perturbations	Aleksandar Bojchevski · Stephan Günnemann
Certified Adversarial Robustness with Additive Noise	Bai Li · Changyou Chen · Wenlin Wang · Lawrence Carin
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training	Haichao Zhang · Jianyu Wang
Efficient online learning with kernels for adversarial large scale problems	Rémi Jézéquel · Pierre Gaillard · Alessandro Rudi
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness	Saeed Mahloujifar · Xiao Zhang · Mohammad Mahmoody · David Evans
Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks	Gunjan Verma · Ananthram Swami
Learning from Bad Data via Generation	Tianyu Guo · Chang Xu · Boxin Shi · Chao Xu · Dacheng Tao
Multi-marginal Wasserstein GAN	Jiezhang Cao · Langyuan Mo · Yifan Zhang · Kui Jia · Chunhua Shen · Mingkui Tan
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies	Bao Wang · Zuoqiang Shi · Stanley Osher
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks	Yiwen Guo · Ziang Yan · Changshui Zhang
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization	Xiangyi Chen · Sijia Liu · Kaidi Xu · Xingguo Li · Xue Lin · Mingyi Hong · David Cox
Adversarial Music: Real world Audio Adversary against Wake-word Detection System	Juncheng Li · Shuhui Qu · Xinjian Li · Joseph Szurley · J. Zico Kolter · Florian Metze
Adversarial Robustness through Local Linearization	Chongli Qin · James Martens · Sven Gowal · Dilip Krishnan · Krishnamurthy Dvijotham · Alhussein Fawzi · Soham De · Robert Stanforth · Pushmeet Kohli
Are Labels Required for Improving Adversarial Robustness?	Jean-Baptiste Alayrac · Jonathan Uesato · Po-Sen Huang · Alhussein Fawzi · Robert Stanforth · Pushmeet Kohli
Certifying Geometric Robustness of Neural Networks	Mislav Balunovic · Maximilian Baader · Gagandeep Singh · Timon Gehr · Martin Vechev
Cross-Domain Transferability of Adversarial Perturbations	Muhammad Muzammal Naseer · Salman H Khan · Muhammad Haris Khan · Fahad Shahbaz Khan · Fatih Porikli
Functional Adversarial Attacks	Cassidy Laidlaw · Soheil Feizi
Improving Black-box Adversarial Attacks with a Transfer-based Prior	Shuyu Cheng · Yinpeng Dong · Tianyu Pang · Hang Su · Jun Zhu
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness	Fanny Yang · Zuowen Wang · Christina Heinze-Deml
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder	Ji Feng · Qi-Zhi Cai · Zhi-Hua Zhou
On Robustness to Adversarial Examples and Polynomial Optimization	Pranjal Awasthi · Abhratanu Dutta · Aravindan Vijayaraghavan
Outlier-robust estimation of a sparse linear model using \ell_1-penalized Huber's M-estimator	Arnak Dalalyan · Philip Thompson
Policy Poisoning in Batch Reinforcement Learning and Control	Yuzhe Ma · Xuezhou Zhang · Wen Sun · Jerry Zhu
Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes	Matt Jordan · Justin Lewis · Alexandros Dimakis
Provably robust boosted decision stumps and trees against adversarial attacks	Maksym Andriushchenko · Matthias Hein
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers	Hadi Salman · Jerry Li · Ilya Razenshteyn · Pengchuan Zhang · Huan Zhang · Sebastien Bubeck · Greg Yang
Robust Attribution Regularization	Jiefeng Chen · Xi Wu · Vaibhav Rastogi · Yingyu Liang · Somesh Jha
Robustness Verification of Tree-based Models	Hongge Chen · Huan Zhang · Si Si · Yang Li · Duane Boning · Cho-Jui Hsieh

AutoML  [Top]

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs	Muhan Zhang · Shali Jiang · Zhicheng Cui · Roman Garnett · Yixin Chen
DATA: Differentiable ArchiTecture Approximation	Jianlong Chang · xinbang zhang · Yiwen Guo · GAOFENG MENG · SHIMING XIANG · Chunhong Pan
DetNAS: Backbone Search for Object Detection	Yukang Chen · Tong Yang · Xiangyu Zhang · GAOFENG MENG · Xinyu Xiao · Jian Sun
Discovering Neural Wirings	Mitchell Wortsman · Ali Farhadi · Mohammad Rastegari
Fast AutoAugment	Sungbin Lim · Ildoo Kim · Taesup Kim · Chiheon Kim · Sungwoong Kim
Meta-Surrogate Benchmarking for Hyperparameter Optimization	Aaron Klein · Zhenwen Dai · Frank Hutter · Neil Lawrence · Javier Gonzalez
NAT: Neural Architecture Transformer for Accurate and Compact Architectures	Yong Guo · Yin Zheng · Mingkui Tan · Qi Chen · Jian Chen · Peilin Zhao · Junzhou Huang
Network Pruning via Transformable Architecture Search	Xuanyi Dong · Yi Yang
Scalable Global Optimization via Local Bayesian Optimization	David Eriksson · Michael Pearce · Jacob Gardner · Ryan Turner · Matthias Poloczek
XNAS: Neural Architecture Search with Expert Advice	Niv Nayman · Asaf Noy · Tal Ridnik · Itamar Friedman · Rong Jin · Lihi Zelnik
Efficient Forward Architecture Search	Hanzhang Hu · John Langford · Rich Caruana · Saurajit Mukherjee · Eric Horvitz · Debadeepta Dey
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection	Junran Peng · Ming Sun · ZHAO-XIANG ZHANG · Tieniu Tan · Junjie Yan
Hyperparameter Learning via Distributional Transfer	Ho Chung Law · Peilin Zhao · Leung Sing Chan · Junzhou Huang · Dino Sejdinovic
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning	Valerio Perrone · Huibin Shen · Matthias Seeger · Cedric Archambeau · Rodolphe Jenatton
Meta Architecture Search	Albert Shaw · Wei Wei · Weiyang Liu · Le Song · Bo Dai
Multi-objective Bayesian optimisation with preferences over objectives	Majid Abdolshah · Alistair Shilton · Santu Rana · Sunil Gupta · Svetha Venkatesh
Practical Two-Step Lookahead Bayesian Optimization	Jian Wu · Peter Frazier
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration	Robert Kleinberg · Kevin Leyton-Brown · Brendan Lucier · Devon Graham
Splitting Steepest Descent for Growing Neural Architectures	Lemeng Wu · Dilin Wang · Qiang Liu
Towards modular and programmable architecture search	Renato Negrinho · Matthew Gormley · Geoffrey Gordon · Darshan Patil · Nghia Le · Daniel Ferreira

Bandit Algorithms  [Top]

Blocking Bandits	Soumya Basu · Rajat Sen · Sujay Sanghavi · Sanjay Shakkottai
Combinatorial Bandits with Relative Feedback	Aadirupa Saha · Aditya Gopalan
Decentralized Cooperative Stochastic Bandits	David Martínez-Rubio · Varun Kanade · Patrick Rebeschini
Doubly-Robust Lasso Bandit	Gi-Soo Kim · Myunghee Cho Paik
Efficient Pure Exploration in Adaptive Round model	tianyuan jin · Jieming SHI · Xiaokui Xiao · Enhong Chen
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits	Sivan Sabato
Nonstochastic Multiarmed Bandits with Unrestricted Delays	Tobias Sommer Thune · Nicolò Cesa-Bianchi · Yevgeny Seldin
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems	Baekjin Kim · Ambuj Tewari
Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints	David Simchi-Levi · Yunzong Xu
Polynomial Cost of Adaptation for X-Armed Bandits	Hedi Hadiji
Semi-Parametric Dynamic Contextual Pricing	Virag Shah · Ramesh Johari · Jose Blanchet
Bayesian Optimization under Heavy-tailed Payoffs	Sayak Ray Chowdhury · Aditya Gopalan
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio	Julian Zimmert · Tor Lattimore
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits	Yogev Bar-On · Yishay Mansour
Learning Multiple Markov Chains via Adaptive Allocation	Mohammad Sadegh Talebi · Odalric-Ambrym Maillard
Linear Stochastic Bandits Under Safety Constraints	Sanae Amani · Mahnoosh Alizadeh · Christos Thrampoulidis
Personalizing Many Decisions with High-Dimensional Covariates	Nima Hamidi · Mohsen Bayati · Kapil Gupta
Non-Asymptotic Pure Exploration by Solving Games	Rémy Degenne · Wouter Koolen · Pierre Ménard
Online EXP3 Learning in Adversarial Bandits with Delayed Feedback	Ilai Bistritz · Zhengyuan Zhou · Xi Chen · Nicholas Bambos · Jose Blanchet
Optimal Best Markovian Arm Identification with Fixed Confidence	Vrettos Moulos
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback	Shinji Ito · Daisuke Hatano · Hanna Sumita · Kei Takemura · Takuro Fukunaga · Naonori Kakimura · Ken-Ichi Kawarabayashi
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems	Young Hun Jung · Ambuj Tewari
Thresholding Bandit with Optimal Aggregate Regret	Chao Tao · Saúl Blanco · Jian Peng · Yuan Zhou
Weighted Linear Bandits for Non-Stationary Environments	Yoan Russac · Claire Vernade · Olivier Cappé
Improved Regret Bounds for Bandit Combinatorial Optimization	Shinji Ito · Daisuke Hatano · Hanna Sumita · Kei Takemura · Takuro Fukunaga · Naonori Kakimura · Ken-Ichi Kawarabayashi
Learning in Generalized Linear Contextual Bandits with Stochastic Delays	Zhengyuan Zhou · Renyuan Xu · Jose Blanchet
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing	Jonas Mueller · Vasilis Syrgkanis · Matt Taddy
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking	Sumeet Katariya · Ardhendu Tripathy · Robert Nowak
Model Selection for Contextual Bandits	Dylan Foster · Akshay Krishnamurthy · Haipeng Luo
No-Regret Learning in Unknown Games with Correlated Payoffs	Pier Giuseppe Sessa · Ilija Bogunovic · Maryam Kamgarpour · Andreas Krause
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric	Nirandika Wanigasekara · Christina Yu
Pure Exploration with Multiple Correct Answers	Rémy Degenne · Wouter Koolen
Recovering Bandits	Ciara Pike-Burke · Steffen Grunewalder
Sequential Experimental Design for Transductive Linear Bandits	Lalit Jain · Kevin Jamieson · Tanner Fiez · Lillian Ratliff
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits	Etienne Boursier · Vianney Perchet
Are sample means in multi-armed bandits positively or negatively biased?	Jaehyeok Shin · Aaditya Ramdas · Alessandro Rinaldo
A New Perspective on Pool-Based Active Classification and False-Discovery Control	Lalit Jain · Kevin Jamieson
Bootstrapping Upper Confidence Bound	Botao Hao · Yasin Abbasi Yadkori · Zheng Wen · Guang Cheng
Categorized Bandits	Matthieu Jedor · Vianney Perchet · Jonathan Louedec
Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback	Arun Verma · Manjesh Hanawal · Arun Rajkumar · Raman Sankaran
Contextual Bandits with Cross-Learning	Santiago Balseiro · Negin Golrezaei · Mohammad Mahdian · Vahab Mirrokni · Jon Schneider
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards	Anmol Kagrecha · Jayakrishnan Nair · Krishna Jagannathan
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions	Negin Golrezaei · Adel Javanmard · Vahab Mirrokni
Stochastic Bandits with Context Distributions	Johannes Kirschner · Andreas Krause
Thompson Sampling and Approximate Inference	My Phan · Yasin Abbasi Yadkori · Justin Domke
Thompson Sampling for Multinomial Logit Contextual Bandits	Min-hwan Oh · Garud Iyengar
Thompson Sampling with Information Relaxation Penalties	Seungki Min · Costis Maglaras · Ciamac C Moallemi

Boosting and Ensemble Methods  [Top]

A Debiased MDI Feature Importance Measure for Random Forests	Xiao Li · Yu Wang · Sumanta Basu · Karl Kumbier · Bin Yu
A Refined Margin Distribution Analysis for Forest Representation Learning	Shen-Huan Lyu · Liang Yang · Zhi-Hua Zhou
Faster Boosting with Smaller Memory	Julaiti Alafate · Yoav S Freund
Margin-Based Generalization Lower Bounds for Boosted Classifiers	Allan Grønlund · Lior Kamma · Kasper Green Larsen · Alexander Mathiasen · Jelani Nelson
Minimal Variance Sampling in Stochastic Gradient Boosting	Bulat Ibragimov · Gleb Gusev
MonoForest framework for tree ensemble analysis	Igor Kuralenok · Vasilii Ershov · Igor Labutin
Regularized Gradient Boosting	Corinna Cortes · Mehryar Mohri · Dmitry Storcheus
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach	Peilin Zhong · Yuchen Mo · Chang Xiao · Pengyu Chen · Changxi Zheng

Classification  [Top]

Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components	Sascha Saralajew · Lars Holdijk · Maike Rees · Ebubekir Asan · Thomas Villmann
Multilabel reductions: what is my loss optimising?	Aditya Menon · Ankit Singh Rawat · Sashank Reddi · Sanjiv Kumar
Optimal Sparse Decision Trees	Xiyang Hu · Cynthia Rudin · Margo Seltzer
Data Cleansing for Models Trained with SGD	Satoshi Hara · Atsushi Nitanda · Takanori Maehara
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise	Yilun Xu · Peng Cao · Yuqing Kong · Yizhou Wang
Copula Multi-label Learning	Weiwei Liu
Optimizing Generalized Rate Metrics with Three Players	Harikrishna Narasimhan · Andrew Cotter · Maya Gupta

Clustering  [Top]

Coresets for Clustering with Fairness Constraints	Lingxiao Huang · Shaofeng Jiang · Nisheeth Vishnoi
Correlation Clustering with Adaptive Similarity Queries	Marco Bressan · Nicolò Cesa-Bianchi · Andrea Paudice · Fabio Vitale
Correlation clustering with local objectives	Sanchit Kalhan · Konstantin Makarychev · Timothy Zhou
Foundations of Comparison-Based Hierarchical Clustering	Debarghya Ghoshdastidar · Michaël Perrot · Ulrike von Luxburg
Fully Dynamic Consistent Facility Location	Vincent Cohen-Addad · Niklas Oskar D Hjuler · Nikos Parotsidis · David Saulpic · Chris Schwiegelshohn
Greedy Sampling for Approximate Clustering in the Presence of Outliers	Aditya Bhaskara · Sharvaree Vadgama · Hong Xu
k-Means Clustering of Lines for Big Data	Yair Marom · Dan Feldman
Near Neighbor: Who is the Fairest of Them All?	Sariel Har-Peled · Sepideh Mahabadi
Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves	Stefan Meintrup · Alexander Munteanu · Dennis Rohde
Same-Cluster Querying for Overlapping Clusters	Wasim Huleihel · Arya Mazumdar · Muriel Medard · Soumyabrata Pal
Selective Sampling-based Scalable Sparse Subspace Clustering	Shin Matsushima · Maria Brbic
Spectral Modification of Graphs for Improved Spectral Clustering	Ioannis Koutis · Huong Le
Subquadratic High-Dimensional Hierarchical Clustering	Amir Abboud · Vincent Cohen-Addad · Hussein Houdrouge
Ultra Fast Medoid Identification via Correlated Sequential Halving	Tavor Baharav · David Tse
Ultrametric Fitting by Gradient Descent	Giovanni Chierchia · Benjamin Perret

Collaborative Filtering  [Top]

Markov Random Fields for Collaborative Filtering	Harald Steck
Regularized Weighted Low Rank Approximation	Frank Ban · David Woodruff · Richard Zhang

Components Analysis (e.g., CCA, ICA, LDA, PCA)  [Top]

Backpropagation-Friendly Eigendecomposition	Wei Wang · Zheng Dang · Yinlin Hu · Pascal Fua · Mathieu Salzmann
Learning-Based Low-Rank Approximations	Piotr Indyk · Ali Vakilian · Yang Yuan
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery	Chenwei DING · Mingming Gong · Kun Zhang · Dacheng Tao
Sobolev Independence Criterion	Youssef Mroueh · Tom Sercu · Mattia Rigotti · Inkit Padhi · Cicero Nogueira dos Santos
Towards a Zero-One Law for Column Subset Selection	Zhao Song · David Woodruff · Peilin Zhong
Average Case Column Subset Selection for Entrywise \ell_1-Norm Loss	Zhao Song · David Woodruff · Peilin Zhong
On Distributed Averaging for Stochastic k-PCA	Aditya Bhaskara · Pruthuvi Maheshakya Wijewardena
On Robustness of Principal Component Regression	Anish Agarwal · Devavrat Shah · Dennis Shen · Dogyoon Song
Towards Practical Alternating Least-Squares for CCA	Zhiqiang Xu · Ping Li
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis	David Clark · Jesse Livezey · Kristofer Bouchard

Density Estimation  [Top]

Fisher Efficient Inference of Intractable Models	Song Liu · Takafumi Kanamori · Wittawat Jitkrittum · Yu Chen
Learning Distributions Generated by One-Layer ReLU Networks	Shanshan Wu · Alexandros Dimakis · Sujay Sanghavi
On Fenchel Mini-Max Learning	Chenyang Tao · Liqun Chen · Shuyang Dai · Junya Chen · Ke Bai · Dong Wang · Jianfeng Feng · Wenlian Lu · Georgiy Bobashev · Lawrence Carin
Practical and Consistent Estimation of f-Divergences	Paul Rubenstein · Olivier Bousquet · Josip Djolonga · Carlos Riquelme · Ilya Tolstikhin
Re-examination of the Role of Latent Variables in Sequence Modeling	Guokun Lai · Zihang Dai · Yiming Yang · Shinjae Yoo
Space and Time Efficient Kernel Density Estimation in High Dimensions	Arturs Backurs · Piotr Indyk · Tal Wagner
Unconstrained Monotonic Neural Networks	Antoine Wehenkel · Gilles Louppe

Dynamical Systems  [Top]

Mutually Regressive Point Processes	Ifigeneia Apostolopoulou · Scott Linderman · Kyle Miller · Artur Dubrawski
Neural Networks with Cheap Differential Operators	Tian Qi Chen · David Duvenaud

Few-Shot Learning  [Top]

Adaptive Cross-Modal Few-shot Learning	Chen Xing · Negar Rostamzadeh · Boris Oreshkin · Pedro O. Pinheiro
Cross Attention Network for Few-shot Classification	Ruibing Hou · Hong Chang · Bingpeng MA · Shiguang Shan · Xilin Chen
Incremental Few-Shot Learning with Attention Attractor Networks	Mengye Ren · Renjie Liao · Ethan Fetaya · Richard Zemel
Learning to Self-Train for Semi-Supervised Few-Shot Classification	Xinzhe Li · Qianru Sun · Yaoyao Liu · Qin Zhou · Shibao Zheng · Tat-Seng Chua · Bernt Schiele
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition	Satoshi Tsutsui · Yanwei Fu · David Crandall
Transductive Zero-Shot Learning with Visual Structure Constraint	Ziyu Wan · Dongdong Chen · Yan Li · Xingguang Yan · Junge Zhang · Yizhou Yu · Jing Liao
Unsupervised Meta-Learning for Few-Shot Image Classification	Siavash Khodadadeh · Ladislau Boloni · Mubarak Shah
Zero-shot Learning via Simultaneous Generating and Learning	Hyeonwoo Yu · Beomhee Lee

Kernel Methods  [Top]

Convergence Guarantees for Adaptive Bayesian Quadrature Methods	Motonobu Kanagawa · Philipp Hennig
Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses	Ulysse Marteau-Ferey · Francis Bach · Alessandro Rudi
Kernel Instrumental Variable Regression	Rahul Singh · Maneesh Sahani · Arthur Gretton
Kernel Stein Tests for Multiple Model Comparison	Jen Ning Lim · Makoto Yamada · Bernhard Schölkopf · Wittawat Jitkrittum
On Exact Computation with an Infinitely Wide Neural Net	Sanjeev Arora · Simon Du · Wei Hu · Zhiyuan Li · Russ Salakhutdinov · Ruosong Wang
Solving Interpretable Kernel Dimensionality Reduction	Chieh Wu · Jared Miller · Yale Chang · Mario Sznaier · Jennifer Dy
Comparing distributions: \ell_1 geometry improves kernel two-sample testing	meyer scetbon · Gael Varoquaux
Distributionally Robust Optimization and Generalization in Kernel Methods	Matthew Staib · Stefanie Jegelka
Learning metrics for persistence-based summaries and applications for graph classification	Qi Zhao · Yusu Wang
Minimum Stein Discrepancy Estimators	Alessandro Barp · Francois-Xavier Briol · Andrew Duncan · Mark Girolami · Lester Mackey
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels	Michela Meister · Tamas Sarlos · David Woodruff
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test	Lizhong Ding · Mengyang Yu · Li Liu · Fan Zhu · Yong Liu · Yu Li · Ling Shao
Wasserstein Weisfeiler-Lehman Graph Kernels	Matteo Togninalli · Elisabetta Ghisu · Felipe Llinares-López · Bastian Rieck · Karsten Borgwardt

Large Scale Learning  [Top]

A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization	Sulaiman Alghunaim · Kun Yuan · Ali H Sayed
Asymptotics for Sketching in Least Squares Regression	Edgar Dobriban · Sifan Liu
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation	Shashank Rajput · Hongyi Wang · Zachary Charles · Dimitris Papailiopoulos
Large-scale optimal transport map estimation using projection pursuit	Cheng Meng · Yuan Ke · Jingyi Zhang · Mengrui Zhang · Wenxuan Zhong · Ping Ma
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond	Lin Chen · Hossein Esfandiari · Gang Fu · Vahab Mirrokni
Massively scalable Sinkhorn distances via the Nyström method	Jason Altschuler · Francis Bach · Alessandro Rudi · Jonathan Niles-Weed
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods	Belhal Karimi · Hoi-To Wai · Eric Moulines · Marc Lavielle
Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation	zengfeng Huang · Ziyue Huang · Yilei WANG · Ke Yi
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations	Debraj Basu · Deepesh Data · Can Karakus · Suhas Diggavi
Random Projections with Asymmetric Quantization	Xiaoyun Li · Ping Li
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling	Ping Li · Xiaoyun Li · Cun-Hui Zhang
Robust and Communication-Efficient Collaborative Learning	Amirhossein Reisizadeh · Hossein Taheri · Aryan Mokhtari · Hamed Hassani · Ramtin Pedarsani
Sampled Softmax with Random Fourier Features	Ankit Singh Rawat · Jiecao Chen · Felix Xinnan Yu · Ananda Theertha Suresh · Sanjiv Kumar
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products	Tharun Kumar Reddy Medini · Qixuan Huang · Yiqiu Wang · Vijai Mohan · Anshumali Shrivastava
Sliced Gromov-Wasserstein	Vayer Titouan · Rémi Flamary · Nicolas Courty · Romain Tavenard · Laetitia Chapel
SySCD: A System-Aware Parallel Coordinate Descent Algorithm	Nikolas Ioannou · Celestine Mendler-Dünner · Thomas Parnell
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism	Yanping Huang · Youlong Cheng · Ankur Bapna · Orhan Firat · Dehao Chen · Mia Chen · HyoukJoong Lee · Jiquan Ngiam · Quoc V Le · Yonghui Wu · zhifeng Chen

Meta-Learning  [Top]

Adaptive Gradient-Based Meta-Learning Methods	Mikhail Khodak · Maria-Florina Balcan · Ameet Talwalkar
Guided Meta-Policy Search	Russell Mendonca · Abhishek Gupta · Rosen Kralev · Pieter Abbeel · Sergey Levine · Chelsea Finn
Learning to Propagate for Graph Meta-Learning	LU LIU · Tianyi Zhou · Guodong Long · Jing Jiang · Chengqi Zhang
Meta Learning with Relational Information for Short Sequences	Yujia Xie · Haoming Jiang · Feng Liu · Tuo Zhao · Hongyuan Zha
Meta-Curvature	Eunbyung Park · Junier Oliva
Meta-Learning Representations for Continual Learning	Khurram Javed · Martha White
Meta-Learning with Implicit Gradients	Aravind Rajeswaran · Chelsea Finn · Sham Kakade · Sergey Levine
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting	Jun Shu · Qi Xie · Lixuan Yi · Qian Zhao · Sanping Zhou · Zongben Xu · Deyu Meng
Self-Supervised Generalisation with Meta Auxiliary Learning	Shikun Liu · Andrew Davison · Edward Johns
Efficient Meta Learning via Minibatch Proximal Update	Pan Zhou · Xiaotong Yuan · Huan Xu · Shuicheng Yan · Jiashi Feng
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes	James Requeima · Jonathan Gordon · John Bronskill · Sebastian Nowozin · Richard Turner
Learning to Learn By Self-Critique	Antreas Antoniou · Amos Storkey
MetaInit: Initializing learning by learning to initialize	Yann Dauphin · Samuel Schoenholz
Metalearned Neural Memory	Tsendsuren Munkhdalai · Alessandro Sordoni · TONG WANG · Adam Trischler
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation	Risto Vuorio · Shao-Hua Sun · Hexiang Hu · Joseph Lim
Neural Relational Inference with Fast Modular Meta-learning	Ferran Alet · Erica Weng · Tomás Lozano-Pérez · Leslie Kaelbling
Online-Within-Online Meta-Learning	Giulia Denevi · Dimitris Stamos · Carlo Ciliberto · Massimiliano Pontil
Learning to Optimize in Swarms	Yue Cao · Tianlong Chen · Zhangyang Wang · Yang Shen
Unsupervised Curricula for Visual Meta-Reinforcement Learning	Allan Jabri · Kyle Hsu · Abhishek Gupta · Ben Eysenbach · Sergey Levine · Chelsea Finn

Metric Learning  [Top]

Curvilinear Distance Metric Learning	Shuo Chen · Lei Luo · Jian Yang · Chen Gong · Jun Li · Heng Huang
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data	Han Liu · Zhizhong Han · Yu-Shen Liu · Ming Gu
Metric Learning for Adversarial Robustness	Chengzhi Mao · Ziyuan Zhong · Junfeng Yang · Carl Vondrick · Baishakhi Ray

Missing Data  [Top]

Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption

Wei Ma · George H Chen

Model Selection and Structure Learning  [Top]

An Adaptive Empirical Bayesian Method for Sparse Deep Learning	Wei Deng · Xiao Zhang · Faming Liang · Guang Lin
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters	XIA XIAO · Zigeng Wang · Sanguthevar Rajasekaran
Constraint-based Causal Structure Learning with Consistent Separating Sets	Honghao Li · Vincent Cabeli · Nadir Sella · Herve Isambert
Fast structure learning with modular regularization	Greg Ver Steeg · Hrayr Harutyunyan · Daniel Moyer · Aram Galstyan
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer	Zhiyong Yang · Qianqian Xu · Yangbangyan Jiang · Xiaochun Cao · Qingming Huang
Learning Erdos-Renyi Random Graphs via Edge Detecting Queries	Zihan Li · Matthias Fresacher · Jonathan Scarlett
Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions	Chris Russell · Matteo Toso · Neill Campbell

Multitask and Transfer Learning  [Top]

Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning	Xinyang Chen · Sinan Wang · Bo Fu · Mingsheng Long · Jianmin Wang
Continual Unsupervised Representation Learning	Dushyant Rao · Francesco Visin · Andrei Rusu · Razvan Pascanu · Yee Whye Teh · Raia Hadsell
Gradient based sample selection for online continual learning	Rahaf Aljundi · Min Lin · Baptiste Goujaud · Yoshua Bengio
Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks	Joshua Lee · Prasanna Sattigeri · Gregory Wornell
Learning Sample-Specific Models with Low-Rank Personalized Regression	Ben Lengerich · Bryon Aragam · Eric Xing
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies	Xue Bin Peng · Michael Chang · Grace Zhang · Pieter Abbeel · Sergey Levine
Transfer Anomaly Detection by Inferring Latent Domain Representations	Atsutoshi Kumagai · Tomoharu Iwata · Yasuhiro Fujiwara
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks	Ximei Wang · Ying Jin · Mingsheng Long · Jianmin Wang · Michael Jordan
Uncertainty-based Continual Learning with Adaptive Regularization	Hongjoon Ahn · Sungmin Cha · Donggyu Lee · Taesup Moon
Better Transfer Learning with Inferred Successor Maps	Tamas Madarasz · Tim Behrens
Compacting, Picking and Growing for Unforgetting Continual Learning	Ching-Yi Hung · Cheng-Hao Tu · Cheng-En Wu · Chien-Hung Chen · Yi-Ming Chan · Chu-Song Chen
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift	Stephan Rabanser · Stephan Günnemann · Zachary Lipton
Generalization in multitask deep neural classifiers: a statistical physics approach	Anthony Ndirango · Tyler Lee
Hierarchical Optimal Transport for Multimodal Distribution Alignment	John Lee · Max Dabagia · Eva Dyer · Christopher Rozell
Learning Robust Global Representations by Penalizing Local Predictive Power	Haohan Wang · Songwei Ge · Zachary Lipton · Eric Xing
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains	Elliot Meyerson · Risto Miikkulainen
Online Continual Learning with Maximal Interfered Retrieval	Rahaf Aljundi · Eugene Belilovsky · Tinne Tuytelaars · Laurent Charlin · Massimo Caccia · Min Lin · Lucas Page-Caccia
Pareto Multi-Task Learning	Xi Lin · Hui-Ling Zhen · Zhenhua Li · Qing-Fu Zhang · Sam Kwong
Transfer Learning via Minimizing the Performance Gap Between Domains	Boyu Wang · Jorge Mendez · Mingbo Cai · Eric Eaton

Nonlinear Dimensionality Reduction and Manifold Learning  [Top]

Dimensionality reduction: theoretical perspective on practical measures	Yair Bartal · Nova Fandina · Ofer Neiman
Learning nonlinear level sets for dimensionality reduction in function approximation	Guannan Zhang · Jiaxin Zhang · Jacob Hinkle
No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms	Max Vladymyrov
Selecting the independent coordinates of manifolds with large aspect ratios	Yu-Chia Chen · Marina Meila
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections	Boris Muzellec · Marco Cuturi
Unsupervised Co-Learning on G-Manifolds Across Irreducible Representations	Yifeng Fan · Tingran Gao · Zhizhen Jane Zhao

Online Learning  [Top]

Bandits with Feedback Graphs and Switching Costs	Raman Arora · Teodor Vanislavov Marinov · Mehryar Mohri
Batched Multi-armed Bandits Problem	Zijun Gao · Yanjun Han · Zhimei Ren · Zhengqing Zhou
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization	Gautam Goel · Yiheng Lin · Haoyuan Sun · Adam Wierman
Equipping Experts/Bandits with Long-term Memory	Kai Zheng · Haipeng Luo · Ilias Diakonikolas · Liwei Wang
Large Scale Markov Decision Processes with Changing Rewards	Adrian Rivera Cardoso · He Wang · Huan Xu
Online Learning via the Differential Privacy Lens	Jacob Abernethy · Young Hun Jung · Chansoo Lee · Audra McMillan · Ambuj Tewari
Online Normalization for Training Neural Networks	Vitaliy Chiley · Ilya Sharapov · Atli Kosson · Urs Koster · Ryan Reece · Sofia Samaniego de la Fuente · Vishal Subbiah · Michael James
Online Prediction of Switching Graph Labelings with Cluster Specialists	Mark Herbster · James Robinson
Secretary Ranking with Minimal Inversions	Sepehr Assadi · Eric Balkanski · Renato Leme
Dying Experts: Efficient Algorithms with Optimal Regret Bounds	Hamid Shayestehmanesh · Sajjad Azami · Nishant Mehta
Dynamic Local Regret for Non-convex Online Forecasting	Sergul Aydore · Tianhao Zhu · Dean Foster
Online Convex Matrix Factorization with Representative Regions	Jianhao Peng · Olgica Milenkovic · Abhishek Agarwal
Online Forecasting of Total-Variation-bounded Sequences	Dheeraj Baby · Yu-Xiang Wang
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function	Aviv Rosenberg · Yishay Mansour
Private Learning Implies Online Learning: An Efficient Reduction	Alon Gonen · Elad Hazan · Shay Moran
Random Path Selection for Continual Learning	Jathushan Rajasegaran · Munawar Hayat · Salman H Khan · Fahad Shahbaz Khan · Ling Shao
Superposition of many models into one	Brian Cheung · Alexander Terekhov · Yubei Chen · Pulkit Agrawal · Bruno Olshausen
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning	Dirk van der Hoeven

Ranking and Preference Learning  [Top]

Learning Mixtures of Plackett-Luce Models from Structured Partial Orders	Zhibing Zhao · Lirong Xia
On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons	Wenbo Ren · Jia (Kevin) Liu · Ness Shroff
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression	Ruidi Chen · Ioannis Paschalidis

Regression  [Top]

Iterative Least Trimmed Squares for Mixed Linear Regression	Yanyao Shen · Sujay Sanghavi
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights	Maria Jahja · David Farrow · Roni Rosenfeld · Ryan Tibshirani
Multivariate Distributionally Robust Convex Regression under Absolute Error Loss	Jose Blanchet · Peter W Glynn · Jun Yan · Zhengqing Zhou
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation	Huaian Diao · Rajesh Jayaram · Zhao Song · Wen Sun · David Woodruff
Fast and Accurate Least-Mean-Squares Solvers	Ibrahim Jubran · Alaa Maalouf · Dan Feldman
Partitioning Structure Learning for Segmented Linear Regression Trees	Xiangyu Zheng · Song Xi Chen
Sparse High-Dimensional Isotonic Regression	David Gamarnik · Julia Gaudio
Total Least Squares Regression in Input Sparsity Time	Huaian Diao · Zhao Song · David Woodruff · Xin Yang

Relational Learning  [Top]

A Flexible Generative Framework for Graph-based Semi-supervised Learning	Jiaqi Ma · Weijing Tang · Ji Zhu · Qiaozhu Mei
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling	Ming Hou · Jiajia Tang · Jianhai Zhang · Wanzeng Kong · Qibin Zhao
Diffusion Improves Graph Learning	Johannes Klicpera · Stefan Weißenberger · Stephan Günnemann
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels	Simon Du · Kangcheng Hou · Russ Salakhutdinov · Barnabas Poczos · Ruosong Wang · Keyulu Xu
Hyperbolic Graph Convolutional Neural Networks	Ines Chami · Zhitao Ying · Christopher Ré · Jure Leskovec
Hyperbolic Graph Neural Networks	Qi Liu · Maximilian Nickel · Douwe Kiela
Implicitly learning to reason in first-order logic	Vaishak Belle · Brendan Juba
Learning Disentangled Representations for Recommendation	Jianxin Ma · Chang Zhou · Peng Cui · Hongxia Yang · Wenwu Zhu
Multi-relational Poincaré Graph Embeddings	Ivana Balazevic · Carl Allen · Timothy Hospedales
On the equivalence between graph isomorphism testing and function approximation with GNNs	Zhengdao Chen · Soledad Villar · Lei Chen · Joan Bruna
Probabilistic Logic Neural Networks for Reasoning	Meng Qu · Jian Tang

Representation Learning  [Top]

Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations	Fenglin Liu · Yuanxin Liu · Xuancheng Ren · Xiaodong He · Xu Sun
Augmented Neural ODEs	Emilien Dupont · Arnaud Doucet · Yee Whye Teh
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs	Denis Mazur · Vage Egiazarian · Stanislav Morozov · Artem Babenko
Exact Rate-Distortion in Autoencoders via Echo Noise	Rob Brekelmans · Daniel Moyer · Aram Galstyan · Greg Ver Steeg
Information Competing Process for Learning Diversified Representations	Jie Hu · Rongrong Ji · ShengChuan Zhang · Xiaoshuai Sun · Qixiang Ye · Chia-Wen Lin · Qi Tian
Learning low-dimensional state embeddings and metastable clusters from time series data	Yifan Sun · Yaqi Duan · Hao Gong · Mengdi Wang
Learning Nonsymmetric Determinantal Point Processes	Mike Gartrell · Victor-Emmanuel Brunel · Elvis Dohmatob · Syrine Krichene
Provably Powerful Graph Networks	Haggai Maron · Heli Ben-Hamu · Hadar Serviansky · Yaron Lipman
Quaternion Knowledge Graph Embeddings	SHUAI ZHANG · Yi Tay · Lina Yao · Qi Liu
Semi-supervisedly Co-embedding Attributed Networks	Zaiqiao Meng · Shangsong Liang · Jinyuan Fang · Teng Xiao
Large Scale Adversarial Representation Learning	Jeff Donahue · Karen Simonyan
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks	Difan Zou · Ziniu Hu · Yewen Wang · Song Jiang · Yizhou Sun · Quanquan Gu
Learning elementary structures for 3D shape generation and matching	Theo Deprelle · Thibault Groueix · Matthew Fisher · Vladimir Kim · Bryan Russell · Mathieu Aubry
Learning from brains how to regularize machines	Zhe Li · Wieland Brendel · Edgar Walker · Erick Cobos · Taliah Muhammad · Jacob Reimer · Matthias Bethge · Fabian Sinz · Zachary Pitkow · Andreas Tolias
Rethinking Kernel Methods for Node Representation Learning on Graphs	Yu Tian · Long Zhao · Xi Peng · Dimitris Metaxas
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices	Vincent Chen · Sen Wu · Alexander Ratner · Jen Weng · Christopher Ré
Deep Supervised Summarization: Algorithm and Application to Learning Instructions	Chengguang Xu · Ehsan Elhamifar
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction	Alban Laflaquière · Michael Garcia Ortiz
Unsupervised State Representation Learning in Atari	Ankesh Anand · Evan Racah · Sherjil Ozair · Yoshua Bengio · Marc-Alexandre Côté · R Devon Hjelm
What the Vec? Towards Probabilistically Grounded Embeddings	Carl Allen · Ivana Balazevic · Timothy Hospedales
Are Disentangled Representations Helpful for Abstract Visual Reasoning?	Sjoerd van Steenkiste · Francesco Locatello · Jürgen Schmidhuber · Olivier Bachem
CPM-Nets: Cross Partial Multi-View Networks	Changqing Zhang · Zongbo Han · yajie cui · Huazhu Fu · Joey Tianyi Zhou · Qinghua Hu
Cross-lingual Language Model Pretraining	Alexis CONNEAU · Guillaume Lample
Graph Transformer Networks	Seongjun Yun · Minbyul Jeong · Raehyun Kim · Jaewoo Kang · Hyunwoo Kim
Learning Representations by Maximizing Mutual Information Across Views	Philip Bachman · R Devon Hjelm · William Buchwalter
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models	Tao Yu · Christopher De Sa
On the Fairness of Disentangled Representations	Francesco Locatello · Gabriele Abbati · Thomas Rainforth · Stefan Bauer · Bernhard Schölkopf · Olivier Bachem
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset	Muhammad Waleed Gondal · Manuel Wuthrich · Djordje Miladinovic · Francesco Locatello · Martin Breidt · Valentin Volchkov · Joel Akpo · Olivier Bachem · Bernhard Schölkopf · Stefan Bauer
Stacked Capsule Autoencoders	Adam Kosiorek · Sara Sabour · Yee Whye Teh · Geoffrey E Hinton
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology	Nima Dehmamy · Albert-Laszlo Barabasi · Rose Yu
Wasserstein Dependency Measure for Representation Learning	Sherjil Ozair · Corey Lynch · Yoshua Bengio · Aaron van den Oord · Sergey Levine · Pierre Sermanet

Semi-Supervised Learning  [Top]

A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning	Xuanqing Liu · Si Si · Jerry Zhu · Yang Li · Cho-Jui Hsieh
Are Anchor Points Really Indispensable in Label-Noise Learning?	Xiaobo Xia · Tongliang Liu · Nannan Wang · Bo Han · Chen Gong · Gang Niu · Masashi Sugiyama
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	Sitao Luan · Mingde Zhao · Xiao-Wen Chang · Doina Precup
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs	Pedro Mercado · Francesco Tudisco · Matthias Hein
Graph Agreement Models for Semi-Supervised Learning	Otilia Stretcu · Krishnamurthy Viswanathan · Dana Movshovitz-Attias · Emmanouil Platanios · Sujith Ravi · Andrew Tomkins
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response	Fan Zhou · Tengfei Li · Haibo Zhou · Hongtu Zhu · Ye Jieping
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs	Naganand Yadati · Madhav Nimishakavi · Prateek Yadav · Vikram Nitin · Anand Louis · Partha Talukdar
A Condition Number for Joint Optimization of Cycle-Consistent Networks	Leonidas J Guibas · Qixing Huang · Zhenxiao Liang
MixMatch: A Holistic Approach to Semi-Supervised Learning	David Berthelot · Nicholas Carlini · Ian Goodfellow · Nicolas Papernot · Avital Oliver · Colin A Raffel
Uncoupled Regression from Pairwise Comparison Data	Liyuan Xu · Junya Honda · Gang Niu · Masashi Sugiyama
Unlabeled Data Improves Adversarial Robustness	Yair Carmon · Aditi Raghunathan · Ludwig Schmidt · John Duchi · Percy Liang

Similarity and Distance Learning  [Top]

A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport	Arun Jambulapati · Aaron Sidford · Kevin Tian
Conditional Independence Testing using Generative Adversarial Networks	Alexis Bellot · Mihaela van der Schaar
Generalized Sliced Wasserstein Distances	Soheil Kolouri · Kimia Nadjahi · Umut Simsekli · Roland Badeau · Gustavo Rohde
Hyperspherical Prototype Networks	Pascal Mettes · Elise van der Pol · Cees Snoek
Input Similarity from the Neural Network Perspective	Guillaume Charpiat · Nicolas Girard · Loris Felardos · Yuliya Tarabalka
Landmark Ordinal Embedding	Nikhil Ghosh · Yuxin Chen · Yisong Yue
Tree-Sliced Variants of Wasserstein Distances	Tam Le · Makoto Yamada · Kenji Fukumizu · Marco Cuturi

Sparse Coding and Dimensionality Expansion  [Top]

Learning step sizes for unfolded sparse coding

Pierre Ablin · Thomas Moreau · Mathurin Massias · Alexandre Gramfort

Sparsity and Compressed Sensing  [Top]

Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing	Zhiqi Bu · Jason Klusowski · Cynthia Rush · Weijie Su
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors	Gauri Jagatap · Chinmay Hegde
Block Coordinate Regularization by Denoising	Yu Sun · Jiaming Liu · Ulugbek Kamilov
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso	Quentin Bertrand · Mathurin Massias · Alexandre Gramfort · Joseph Salmon
Efficiently Learning Fourier Sparse Set Functions	Andisheh Amrollahi · Amir Zandieh · Michael Kapralov · Andreas Krause
Fast Sparse Group Lasso	Yasutoshi Ida · Yasuhiro Fujiwara · Hisashi Kashima
Global Guarantees for Blind Demodulation with Generative Priors	Paul Hand · Babhru Joshi
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space	Shuo Yang · Yanyao Shen · Sujay Sanghavi
Inverting Deep Generative models, One layer at a time	Qi Lei · Ajil Jalal · Inderjit S Dhillon · Alexandros Dimakis
Manifold denoising by Nonlinear Robust Principal Component Analysis	He Lyu · Ningyu Sha · Shuyang Qin · Ming Yan · Yuying Xie · Rongrong Wang
Rethinking the CSC Model for Natural Images	Dror Simon · Michael Elad
Sample Complexity of Learning Mixture of Sparse Linear Regressions	Akshay Krishnamurthy · Arya Mazumdar · Andrew McGregor · Soumyabrata Pal
Sampling Sketches for Concave Sublinear Functions of Frequencies	Edith Cohen · Ofir Geri
Screening Sinkhorn Algorithm for Regularized Optimal Transport	Mokhtar Z. Alaya · Maxime Berar · Gilles Gasso · Alain Rakotomamonjy
Solving graph compression via optimal transport	Vikas Garg · Tommi Jaakkola
Superset Technique for Approximate Recovery in One-Bit Compressed Sensing	Larkin Flodin · Venkata Gandikota · Arya Mazumdar
Universality in Learning from Linear Measurements	Ehsan Abbasi · Fariborz Salehi · Babak Hassibi

Spectral Methods  [Top]

A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening	Gecia Bravo Hermsdorff · Lee Gunderson
Learning Deterministic Weighted Automata with Queries and Counterexamples	Gail Weiss · Yoav Goldberg · Eran Yahav
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs	Lorenzo Dall'Amico · Romain Couillet · Nicolas Tremblay

Stochastic Methods  [Top]

Efficient Convex Relaxations for Streaming PCA	Raman Arora · Teodor Vanislavov Marinov
Thinning for Accelerating the Learning of Point Processes	Tianbo Li · Yiping Ke
Understanding Sparse JL for Feature Hashing	Meena Jagadeesan

Structured Prediction  [Top]

Deep Set Prediction Networks	Yan Zhang · Jonathon Hare · Adam Prugel-Bennett
Learning Positive Functions with Pseudo Mirror Descent	Yingxiang Yang · Haoxiang Wang · Negar Kiyavash · Niao He
Localized Structured Prediction	Carlo Ciliberto · Francis Bach · Alessandro Rudi
Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms	Vikas Garg · Tamar Pichkhadze
Retrosynthesis Prediction with Conditional Graph Logic Network	Hanjun Dai · Chengtao Li · Connor Coley · Bo Dai · Le Song
Differentiable Ranking and Sorting using Optimal Transport	Marco Cuturi · Olivier Teboul · Jean-Philippe Vert
Globally Optimal Learning for Structured Elliptical Losses	Yoav Wald · Nofar Noy · Gal Elidan · Ami Wiesel
Graph Structured Prediction Energy Networks	Colin Graber · Alexander Schwing
Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks	Amirmohammad Rooshenas · Dongxu Zhang · Gopal Sharma · Andrew McCallum
Structured Prediction with Projection Oracles	Mathieu Blondel

Uncertainty Estimation  [Top]

Accurate Layerwise Interpretable Competence Estimation	Vickram Rajendran · William LeVine
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning	Jeremiah Liu · John Paisley · Marianthi-Anna Kioumourtzoglou · Brent Coull
Addressing Failure Detection by Learning Model Confidence	Charles Corbière · Nicolas THOME · Avner Bar-Hen · Matthieu Cord · Patrick Pérez
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration	Meelis Kull · Miquel Perello Nieto · Markus Kängsepp · Telmo Silva Filho · Hao Song · Peter Flach
Calibration tests in multi-class classification: A unifying framework	David Widmann · Fredrik Lindsten · Dave Zachariah
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift	Jasper Snoek · Yaniv Ovadia · Emily Fertig · Balaji Lakshminarayanan · Sebastian Nowozin · D. Sculley · Joshua Dillon · Jie Ren · Zachary Nado
Computing Full Conformal Prediction Set with Approximate Homotopy	Eugene Ndiaye · Ichiro Takeuchi
Conformalized Quantile Regression	Yaniv Romano · Evan Patterson · Emmanuel Candes
Deep Gamblers: Learning to Abstain with Portfolio Theory	Ziyin Liu · Zhikang Wang · Paul Pu Liang · Russ Salakhutdinov · Louis-Philippe Morency · Masahito Ueda
Likelihood Ratios for Out-of-Distribution Detection	Jie Ren · Peter J. Liu · Emily Fertig · Jasper Snoek · Ryan Poplin · Mark Depristo · Joshua Dillon · Balaji Lakshminarayanan
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections	Raanan Yehezkel Rohekar · Yaniv Gurwicz · Shami Nisimov · Gal Novik
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians	Axel Brando · Jose A Rodriguez · Jordi Vitria · Alberto Rubio Muñoz
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks	Sunil Thulasidasan · Gopinath Chennupati · Jeff Bilmes · Tanmoy Bhattacharya · Sarah Michalak
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees	Muhammad Osama · Dave Zachariah · Peter Stoica
Reliable training and estimation of variance networks	Nicki Skafte · Martin Jørgensen · Søren Hauberg
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	Andrey Malinin · Mark Gales
Single-Model Uncertainties for Deep Learning	Natasa Tagasovska · David Lopez-Paz
The Functional Neural Process	Christos Louizos · Xiahan Shi · Klamer Schutte · Max Welling
Uncertainty on Asynchronous Time Event Prediction	Marin Biloš · Bertrand Charpentier · Stephan Günnemann
Verified Uncertainty Calibration	Ananya Kumar · Percy Liang · Tengyu Ma

Unsupervised Learning  [Top]

DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision	Tam Nguyen · Maximilian Dax · Chaithanya Kumar Mummadi · Nhung Ngo · Thi Hoai Phuong Nguyen · Zhongyu Lou · Thomas Brox
Emergence of Object Segmentation in Perturbed Generative Models	Adam Bielski · Paolo Favaro
On Adversarial Mixup Resynthesis	Christopher Beckham · Sina Honari · Alex Lamb · Vikas Verma · Farnoosh Ghadiri · R Devon Hjelm · Yoshua Bengio · Chris Pal
q-means: A quantum algorithm for unsupervised machine learning	Iordanis Kerenidis · Jonas Landman · Alessandro Luongo · Anupam Prakash
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching	Hongteng Xu · Dixin Luo · Lawrence Carin
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments	Hugo Caselles-Dupré · Michael Garcia Ortiz · David Filliat
Coresets for Archetypal Analysis	Sebastian Mair · Ulf Brefeld
Multivariate Triangular Quantile Maps for Novelty Detection	Jingjing Wang · Sun Sun · Yaoliang Yu
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network	Siqi Wang · Yijie Zeng · Xinwang Liu · En Zhu · Jianping Yin · Chuanfu Xu · Marius Kloft
High-Quality Self-Supervised Deep Image Denoising	Samuli Laine · Tero Karras · Jaakko Lehtinen · Timo Aila
Object landmark discovery through unsupervised adaptation	Enrique Sanchez · Georgios Tzimiropoulos
PIDForest: Anomaly Detection via Partial Identification	Parikshit Gopalan · Vatsal Sharan · Udi Wieder
Robust Principal Component Analysis with Adaptive Neighbors	Rui Zhang · Hanghang Tong
Flexible Modeling of Diversity with Strongly Log-Concave Distributions	Joshua Robinson · Suvrit Sra · Stefanie Jegelka
Putting An End to End-to-End: Gradient-Isolated Learning of Representations	Sindy Löwe · Peter O'Connor · Bastiaan Veeling
Hamiltonian Neural Networks	Samuel Greydanus · Misko Dzamba · Jason Yosinski
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty	Dan Hendrycks · Mantas Mazeika · Saurav Kadavath · Dawn Song
Learning about an exponential amount of conditional distributions	Mohamed Belghazi · Maxime Oquab · David Lopez-Paz
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules	Shengchao Liu · Mehmet F Demirel · Yingyu Liang
Outlier Detection and Robust PCA Using a Convex Measure of Innovation	Mostafa Rahmani · Ping Li

Applications

Activity and Event Recognition  [Top]

More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation	Quanfu Fan · Chun-Fu (Richard) Chen · Hilde Kuehne · Marco Pistoia · David Cox
Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition	Jinwoo Choi · Chen Gao · Joseph C. E. Messou · Jia-Bin Huang
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos	Yitian Yuan · Lin Ma · Jingwen Wang · Wei Liu · Wenwu Zhu
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging	Mathias Perslev · Michael Jensen · Sune Darkner · Poul Jørgen Jennum · Christian Igel

Audio and Speech Processing  [Top]

Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion	Joan Serrà · Santiago Pascual · Carlos Segura Perales
DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging	Matthieu SIMEONI · Sepand Kashani · Paul Hurley · Martin Vetterli
FastSpeech: Fast, Robust and Controllable Text to Speech	Yi Ren · Yangjun Ruan · Xu Tan · Tao Qin · Sheng Zhao · Zhou Zhao · Tie-Yan Liu
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis	Kundan Kumar · Rithesh Kumar · Thibault de Boissiere · Lucas Gestin · Wei Zhen Teoh · Jose Sotelo · Alexandre de Brébisson · Yoshua Bengio · Aaron Courville

Body Pose, Face, and Gesture Analysis  [Top]

Deep Structured Prediction for Facial Landmark Detection	Lisha Chen · Hui Su · Qiang Ji
Dual Variational Generation for Low Shot Heterogeneous Face Recognition	Chaoyou Fu · Xiang Wu · Yibo Hu · Huaibo Huang · Ran He
Face Reconstruction from Voice using Generative Adversarial Networks	Yandong Wen · Bhiksha Raj · Rita Singh
Learning Temporal Pose Estimation from Sparsely-Labeled Videos	Gedas Bertasius · Christoph Feichtenhofer · Du Tran · Jianbo Shi · Lorenzo Torresani
Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition	Xuesong Niu · Hu Han · Shiguang Shan · Xilin Chen
Sim2real transfer learning for 3D human pose estimation: motion to the rescue	Carl Doersch · Andrew Zisserman

Communication- or Memory-Bounded Learning  [Top]

Communication-efficient Distributed SGD with Sketching	Nikita Ivkin · Daniel Rothchild · Enayat Ullah · Vladimir braverman · Ion Stoica · Raman Arora
Order Optimal One-Shot Distributed Learning	Arsalan Sharifnassab · Saber Salehkaleybar · S. Jamaloddin Golestani

Computational Biology and Bioinformatics  [Top]

Cormorant: Covariant Molecular Neural Networks	Brandon Anderson · Truong Son Hy · Risi Kondor
Deep imitation learning for molecular inverse problems	Eric Jonas
End-to-End Learning on 3D Protein Structure for Interface Prediction	Raphael Townshend · Rishi Bedi · Patricia Suriana · Ron Dror
Evaluating Protein Transfer Learning with TAPE	Roshan Rao · Nicholas Bhattacharya · Neil Thomas · Yan Duan · Peter Chen · John Canny · Pieter Abbeel · Yun Song
Generative Models for Graph-Based Protein Design	John Ingraham · Vikas Garg · Regina Barzilay · Tommi Jaakkola
Recurrent Kernel Networks	Dexiong Chen · Laurent Jacob · Julien Mairal

Computational Photography  [Top]

Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization	Miika Aittala · Prafull Sharma · Lukas Murmann · Adam Yedidia · Gregory Wornell · Bill Freeman · Fredo Durand
Reflection Separation using a Pair of Unpolarized and Polarized Images	Youwei Lyu · Zhaopeng Cui · Si Li · Marc Pollefeys · Boxin Shi
Training Image Estimators without Image Ground Truth	Zhihao Xia · Ayan Chakrabarti

Computational Social Science  [Top]

Making the Cut: A Bandit-based Approach to Tiered Interviewing	Candice Schumann · Zhi Lang · Jeffrey Foster · John Dickerson
On Human-Aligned Risk Minimization	Liu Leqi · Adarsh Prasad · Pradeep Ravikumar

Computer Vision  [Top]

DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction	Qiangeng Xu · Weiyue Wang · Duygu Ceylan · Radomir Mech · Ulrich Neumann
DM2C: Deep Mixed-Modal Clustering	Yangbangyan Jiang · Qianqian Xu · Zhiyong Yang · Xiaochun Cao · Qingming Huang
ETNet: Error Transition Network for Arbitrary Style Transfer	Chunjin Song · Zhijie Wu · Yang Zhou · Minglun Gong · Hui Huang
Joint-task Self-supervised Learning for Temporal Correspondence	Xueting Li · Sifei Liu · Shalini De Mello · Xiaolong Wang · Jan Kautz · Ming-Hsuan Yang
Learning Conditional Deformable Templates with Convolutional Networks	Adrian Dalca · Marianne Rakic · John Guttag · Mert Sabuncu
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds	Bo Yang · Jianan Wang · Ronald Clark · Qingyong Hu · Sen Wang · Andrew Markham · Niki Trigoni
Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis	Xihui Liu · Guojun Yin · Jing Shao · Xiaogang Wang · hongsheng Li
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution	Yichao Zhou · Haozhi Qi · Jingwei Huang · Yi Ma
Saccader: Improving Accuracy of Hard Attention Models for Vision	Gamaleldin Elsayed · Simon Kornblith · Quoc V Le
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations	Vincent Sitzmann · Michael Zollhoefer · Gordon Wetzstein
Stand-Alone Self-Attention in Vision Models	Niki Parmar · Prajit Ramachandran · Ashish Vaswani · Irwan Bello · Anselm Levskaya · Jon Shlens
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video	Jiawang Bian · Zhichao Li · Naiyan Wang · Huangying Zhan · Chunhua Shen · Ming-Ming Cheng · Ian Reid
Volumetric Correspondence Networks for Optical Flow	Gengshan Yang · Deva Ramanan
Zero-Shot Semantic Segmentation	Maxime Bucher · Tuan-Hung VU · Matthieu Cord · Patrick Pérez
Adaptive GNN for Image Analysis and Editing	Lingyu Liang · LianWen Jin · Yong Xu
Few-shot Video-to-Video Synthesis	Ting-Chun Wang · Ming-Yu Liu · Andrew Tao · Guilin Liu · Bryan Catanzaro · Jan Kautz
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration	Jianchun Chen · Lingjing Wang · Xiang Li · Yi Fang
Image Synthesis with a Single (Robust) Classifier	Shibani Santurkar · Andrew Ilyas · Dimitris Tsipras · Logan Engstrom · Brandon Tran · Aleksander Madry
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels	Natalia Neverova · David Novotny · Andrea Vedaldi
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion	Yiqi Zhong · Cho-Ying Wu · Suya You · Ulrich Neumann
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries	Fuwen Tan · Paola Cascante-Bonilla · Xiaoxiao Guo · Hui Wu · Song Feng · Vicente Ordonez
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning	Jian Ni · Shanghang Zhang · Haiyong Xie
Guided Similarity Separation for Image Retrieval	Chundi Liu · Guangwei Yu · Maksims Volkovs · Cheng Chang · Himanshu Rai · Junwei Ma · Satya Krishna Gorti
Incremental Scene Synthesis	Benjamin Planche · Xuejian Rong · Ziyan Wu · Srikrishna Karanam · Harald Kosch · YingLi Tian · Jan Ernst · ANDREAS HUTTER
Multi-mapping Image-to-Image Translation via Learning Disentanglement	Xiaoming Yu · Yuanqi Chen · Shan Liu · Thomas Li · Ge Li
Neural Diffusion Distance for Image Segmentation	Jian Sun · Zongben Xu
Predicting the Politics of an Image Using Webly Supervised Data	Christopher Thomas · Adriana Kovashka
Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction	Aleksis Pirinen · Erik Gärtner · Cristian Sminchisescu
Blind Super-Resolution Kernel Estimation using an Internal-GAN	Sefi Bell-Kligler · Assaf Shocher · Michal Irani
Chirality Nets for Human Pose Regression	Raymond Yeh · Yuan-Ting Hu · Alexander Schwing
Explicitly disentangling image content from translation and rotation with spatial-VAE	Tristan Bepler · Ellen Zhong · Kotaro Kelley · Edward Brignole · Bonnie Berger
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs	Yuan Liu · Zehong Shen · Zhixuan Lin · Sida Peng · Hujun Bao · Xiaowei Zhou
Learning to Infer Implicit Surfaces without 3D Supervision	Shichen Liu · Shunsuke Saito · Weikai Chen · Hao Li
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer	Wenzheng Chen · Huan Ling · Jun Gao · Edward Smith · Jaakko Lehtinen · Alec Jacobson · Sanja Fidler
A Self Validation Network for Object-Level Human Attention Estimation	Zehua Zhang · Chen Yu · David Crandall
PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments	Ben Graham · David Novotny · Jeremy Reizenstein
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation	Can Qin · Haoxuan You · Lichen Wang · C.-C. Jay Kuo · Yun Fu
PRNet: Self-Supervised Learning for Partial-to-Partial Registration	Yue Wang · Justin M Solomon
Quadratic Video Interpolation	Xiangyu Xu · Li Siyao · Wenxiu Sun · Qian Yin · Ming-Hsuan Yang
R2D2: Reliable and Repeatable Detector and Descriptor	Jerome Revaud · Cesar De Souza · Martin Humenberger · Philippe Weinzaepfel
Semantic-Guided Multi-Attention Localization for Zero-Shot Learning	Yizhe Zhu · Jianwen Xie · Zhiqiang Tang · Xi Peng · Ahmed Elgammal
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization	Yujiao Shi · Liu Liu · Xin Yu · Hongdong Li
STAR-Caps: Capsule Networks with Straight-Through Attentive Routing	Karim Ahmed · Lorenzo Torresani

Denoising  [Top]

Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images	Magauiya Zhussip · Shakarim Soltanayev · Se Young Chun
Variational Denoising Network: Toward Blind Noise Modeling and Removal	Zongsheng Yue · Hongwei Yong · Qian Zhao · Deyu Meng · Lei Zhang

Dialog- or Communication-Based Learning  [Top]

Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems

Asma Ghandeharioun · Judy Hanwen Shen · Natasha Jaques · Craig Ferguson · Noah Jones · Agata Lapedriza · Rosalind Picard

Fairness, Accountability, and Transparency  [Top]

Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds	Nathan Kallus · Angela Zhou
Assessing Social and Intersectional Biases in Contextualized Word Representations	Yi Chern Tan · L. Elisa Celis
Balancing Efficiency and Fairness in On-Demand Ridesourcing	Nixie S Lesmana · Xuan Zhang · Xiaohui Bei
Characterizing Bias in Classifiers using Generative Models	Daniel McDuff · Shuang Ma · Yale Song · Ashish Kapoor
Demystifying Black-box Models with Symbolic Metamodels	Ahmed Alaa · Mihaela van der Schaar
Envy-Free Classification	Maria-Florina Balcan · Travis Dick · Ritesh Noothigattu · Ariel Procaccia
Fair Algorithms for Clustering	Suman Bera · Deeparnab Chakrabarty · Nicolas Flores · Maryam Negahbani
Modeling Conceptual Understanding in Image Reference Games	Rodolfo Corona Rodriguez · Stephan Alaniz · Zeynep Akata
Multi-Criteria Dimensionality Reduction with Applications to Fairness	Uthaipon Tantipongpipat · Samira Samadi · Mohit Singh · Jamie Morgenstern · Santosh Vempala
Noise-tolerant fair classification	Alex Lamy · Ziyuan Zhong · Aditya Menon · Nakul Verma
On the Accuracy of Influence Functions for Measuring Group Effects	Pang Wei Koh · Kai-Siang Ang · Hubert Teo · Percy Liang
Paradoxes in Fair Machine Learning	Paul Goelz · Anson Kahng · Ariel Procaccia
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness	Yongkai Wu · Lu Zhang · Xintao Wu · Hanghang Tong
This Looks Like That: Deep Learning for Interpretable Image Recognition	Chaofan Chen · Oscar Li · Daniel Tao · Alina Barnett · Cynthia Rudin · Jonathan K Su
Towards Automatic Concept-based Explanations	Amirata Ghorbani · James Wexler · James Zou · Been Kim
Ask not what AI can do, but what AI should do: Towards a framework of task delegability	Brian Lubars · Chenhao Tan
Attribution-Based Confidence Metric For Deep Neural Networks	Susmit Jha · Sunny Raj · Steven Fernandes · Sumit K Jha · Somesh Jha · Brian Jalaian · Gunjan Verma · Ananthram Swami
Average Individual Fairness: Algorithms, Generalization and Experiments	Saeed Sharifi-Malvajerdi · Michael Kearns · Aaron Roth
Disentangling Influence: Using disentangled representations to audit model predictions	Charles Marx · Richard Phillips · Sorelle Friedler · Carlos Scheidegger · Suresh Venkatasubramanian
Equal Opportunity in Online Classification with Partial Feedback	Yahav Bechavod · Katrina Ligett · Aaron Roth · Bo Waggoner · Steven Wu
Exploring Algorithmic Fairness in Robust Graph Covering Problems	Aida Rahmattalabi · Phebe Vayanos · Anthony Fulginiti · Eric Rice · Bryan Wilder · Amulya Yadav · Milind Tambe
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness	Xueru Zhang · Mohammadmahdi Khaliligarekani · Cem Tekin · mingyan liu
Inherent Tradeoffs in Learning Fair Representations	Han Zhao · Geoff Gordon
Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification	Evgenii Chzhen · Christophe Denis · Mohamed Hebiri · Luca Oneto · Massimiliano Pontil
Offline Contextual Bandits with High Probability Fairness Guarantees	Blossom Metevier · Stephen Giguere · Sarah Brockman · Ari Kobren · Yuriy Brun · Emma Brunskill · Philip Thomas
On Relating Explanations and Adversarial Examples	Alexey Ignatiev · Nina Narodytska · Joao Marques-Silva
On Testing for Biases in Peer Review	Ivan Stelmakh · Nihar Shah · Aarti Singh
On the (In)fidelity and Sensitivity of Explanations	Chih-Kuan Yeh · Cheng-Yu Hsieh · Arun Suggala · David Inouye · Pradeep Ravikumar
Policy Learning for Fairness in Ranking	Ashudeep Singh · Thorsten Joachims
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric	Nathan Kallus · Angela Zhou
Unlocking Fairness: a Trade-off Revisited	Michael Wick · swetasudha panda · Jean-Baptiste Tristan

Game Playing  [Top]

Game Design for Eliciting Distinguishable Behavior

Fan Yang · Liu Leqi · Yifan Wu · Zachary Lipton · Pradeep Ravikumar · Tom M Mitchell · William Cohen

Hardware and Systems  [Top]

A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions	Mejbah Alam · Justin Gottschlich · Nesime Tatbul · Javier Turek · Tim Mattson · Abdullah Muzahid
Coda: An End-to-End Neural Program Decompiler	Cheng Fu · Huili Chen · Haolan Liu · Xinyun Chen · Yuandong Tian · Farinaz Koushanfar · Jishen Zhao
Learning Generalizable Device Placement Algorithms for Distributed Machine Learning	ravichandra addanki · Shaileshh Bojja Venkatakrishnan · Shreyan Gupta · Hongzi Mao · Mohammad Alizadeh
Making AI Forget You: Data Deletion in Machine Learning	Antonio Ginart · Melody Guan · Gregory Valiant · James Zou
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers	Igor Fedorov · Ryan Adams · Matthew Mattina · Paul Whatmough
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic	Arash Ardakani · Zhengyun Ji · Amir Ardakani · Warren Gross
Towards Hardware-Aware Tractable Learning of Probabilistic Models	Laura I Galindez Olascoaga · Wannes Meert · Nimish Shah · Marian Verhelst · Guy Van den Broeck

Health  [Top]

Attentive State-Space Modeling of Disease Progression	Ahmed Alaa · Mihaela van der Schaar
Domain Generalization via Model-Agnostic Learning of Semantic Features	Qi Dou · Daniel Coelho de Castro · Konstantinos Kamnitsas · Ben Glocker
Recurrent Registration Neural Networks for Deformable Image Registration	Robin Sandkühler · Simon Andermatt · Grzegorz Bauman · Sylvia Nyilas · Christoph Jud · Philippe C. Cattin
Transfusion: Understanding Transfer Learning for Medical Imaging	Maithra Raghu · Chiyuan Zhang · Jon Kleinberg · Samy Bengio

Image Segmentation  [Top]

Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation	Qiming ZHANG · Jing Zhang · Wei Liu · Dacheng Tao
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations	Xu Wang · Jingming He · Lin Ma
Learnable Tree Filter for Structure-preserving Feature Transform	Lin Song · Yanwei Li · Zeming Li · Gang Yu · Hongbin Sun · Jian Sun · Nanning Zheng
Memory-oriented Decoder for Light Field Salient Object Detection	Miao Zhang · Jingjing Li · JI WEI · Yongri Piao · Huchuan Lu
Multi-source Domain Adaptation for Semantic Segmentation	Sicheng Zhao · Bo Li · Xiangyu Yue · Yang Gu · Pengfei Xu · Runbo Hu · Hua Chai · Kurt Keutzer
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning	Enrique Fita Sanmartin · Sebastian Damrich · Fred Hamprecht
Region Mutual Information Loss for Semantic Segmentation	Shuai Zhao · Yang Wang · Zheng Yang · Deng Cai
Topology-Preserving Deep Image Segmentation	Xiaoling Hu · Fuxin Li · Dimitris Samaras · Chao Chen
Unsupervised Object Segmentation by Redrawing	Mickaël Chen · Thierry Artières · Ludovic Denoyer

Information Retrieval  [Top]

Cross-Modal Learning with Adversarial Samples	CHAO LI · Shangqian Gao · Cheng Deng · De Xie · Wei Liu
Möbius Transformation for Fast Inner Product Search on Graph	Zhixin Zhou · Shulong Tan · Zhaozhuo Xu · Ping Li
Rand-NSG: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node	Suhas Jayaram Subramanya · Fnu Devvrit · Harsha Vardhan Simhadri · Ravishankar Krishnawamy · Rohan Kadekodi

Matrix and Tensor Factorization  [Top]

Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms	Shahana Ibrahim · Xiao Fu · Nikolaos Kargas · Kejun Huang
Expressive power of tensor-network factorizations for probabilistic modeling	Ivan Glasser · Ryan Sweke · Nicola Pancotti · Jens Eisert · Ignacio Cirac
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery	Jicong Fan · Lijun Ding · Yudong Chen · Madeleine Udell
Multiway clustering via tensor block models	Miaoyan Wang · Yuchen Zeng
Singleshot : a scalable Tucker tensor decomposition	Abraham Traore · Maxime Berar · Alain Rakotomamonjy

Natural Language Processing  [Top]

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems	Alex Wang · Yada Pruksachatkun · Nikita Nangia · Amanpreet Singh · Julian Michael · Felix Hill · Omer Levy · Samuel Bowman
A Tensorized Transformer for Language Modeling	Xindian Ma · Peng Zhang · Shuai Zhang · Nan Duan · Yuexian Hou · Ming Zhou · Dawei Song
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification	Ronghui You · Zihan Zhang · Ziye Wang · Suyang Dai · Hiroshi Mamitsuka · Shanfeng Zhu
Comparing Unsupervised Word Translation Methods Step by Step	Mareike Hartmann · Yova Kementchedjhieva · Anders Søgaard
Glyce: Glyph-vectors for Chinese Character Representations	Yuxian Meng · Wei Wu · Fei Wang · Xiaoya Li · Ping Nie · Fan Yin · Muyu Li · Qinghong Han · Yuxian Meng · Jiwei Li
Hierarchical Optimal Transport for Document Representation	Mikhail Yurochkin · Sebastian Claici · Edward Chien · Farzaneh Mirzazadeh · Justin M Solomon
Improving Textual Network Learning with Variational Homophilic Embeddings	Wenlin Wang · Chenyang Tao · Zhe Gan · Guoyin Wang · Liqun Chen · Xinyuan Zhang · Ruiyi Zhang · Qian Yang · Ricardo Henao · Lawrence Carin
Ouroboros: On Accelerating Training of Transformer-Based Language Models	Qian Yang · Zhouyuan Huo · Wenlin Wang · Lawrence Carin
Fast Structured Decoding for Sequence Models	Zhiqing Sun · Zhuohan Li · Haoqing Wang · Di He · Zi Lin · Zhihong Deng
Can Unconditional Language Models Recover Arbitrary Sentences?	Nishant Subramani · Samuel Bowman · Kyunghyun Cho
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation	Ke Wang · Hang Hua · Xiaojun Wan
Defending Against Neural Fake News	Rowan Zellers · Ari Holtzman · Hannah Rashkin · Yonatan Bisk · Ali Farhadi · Franziska Roesner · Yejin Choi
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)	Mariya Toneva · Leila Wehbe
Invariance and identifiability issues for word embeddings	Rachel Carrington · Karthik Bharath · Simon Preston
Kernelized Bayesian Softmax for Text Generation	Ning Miao · Hao Zhou · Chengqi Zhao · Wenxian Shi · Lei Li
Levenshtein Transformer	Jiatao Gu · Changhan Wang · Junbo Zhao
Neural Machine Translation with Soft Prototype	Yiren Wang · Yingce Xia · Fei Tian · Fei Gao · Tao Qin · Cheng Xiang Zhai · Tie-Yan Liu
Paraphrase Generation with Latent Bag of Words	Yao Fu · Yansong Feng · John Cunningham
Unified Language Model Pre-training for Natural Language Understanding and Generation	Li Dong · Nan Yang · Wenhui Wang · Furu Wei · Xiaodong Liu · Yu Wang · Jianfeng Gao · Ming Zhou · Hsiao-Wuen Hon
XLNet: Generalized Autoregressive Pretraining for Language Understanding	Zhilin Yang · Zihang Dai · Yiming Yang · Jaime Carbonell · Russ Salakhutdinov · Quoc V Le

Network Analysis  [Top]

Adaptive Influence Maximization with Myopic Feedback	Binghui Peng · Wei Chen
Conditional Structure Generation through Graph Variational Generative Adversarial Nets	Carl Yang · Peiye Zhuang · Wenhan Shi · Alan Luu · Pan Li
GOT: An Optimal Transport framework for Graph comparison	Hermina Petric Maretic · Mireille El Gheche · Giovanni Chierchia · Pascal Frossard
KerGM: Kernelized Graph Matching	Zhen Zhang · Yijian Xiang · Lingfei Wu · Bing Xue · Arye Nehorai
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection	Pan Li · I Chien · Olgica Milenkovic
Variational Graph Recurrent Neural Networks	Ehsan Hajiramezanali · Arman Hasanzadeh · Krishna Narayanan · Nick Duffield · Mingyuan Zhou · Xiaoning Qian
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning	Fan-Yun Sun · Meng Qu · Jordan Hoffmann · Chin-Wei Huang · Jian Tang

Object Detection  [Top]

PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points	Siyuan Huang · Yixin Chen · Tao Yuan · Siyuan Qi · Yixin Zhu · Song-Chun Zhu
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution	Thang Vu · Hyunjun Jang · Trung X. Pham · Chang Yoo
Consistency-based Semi-supervised Learning for Object detection	Jisoo Jeong · Seungeui Lee · Jeesoo Kim · Nojun Kwak
FreeAnchor: Learning to Match Anchors for Visual Object Detection	Xiaosong Zhang · Fang Wan · Chang Liu · Rongrong Ji · Qixiang Ye
One-Shot Object Detection with Co-Attention and Co-Excitation	Ting-I Hsieh · Yi-Chen Lo · Hwann-Tzong Chen · Tyng-Luh Liu

Object Recognition  [Top]

Learning Deep Bilinear Transformation for Fine-grained Image Representation	Heliang Zheng · Jianlong Fu · Zheng-Jun Zha · Jiebo Luo
Learning Disentangled Representation for Robust Person Re-identification	Chanho Eom · Bumsub Ham
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss	Kaidi Cao · Colin Wei · Adrien Gaidon · Nikos Arechiga · Tengyu Ma

Privacy, Anonymity, and Security  [Top]

Adversarial Examples Are Not Bugs, They Are Features	Andrew Ilyas · Shibani Santurkar · Dimitris Tsipras · Logan Engstrom · Brandon Tran · Aleksander Madry
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation	Mark Bun · Thomas Steinke
Capacity Bounded Differential Privacy	Kamalika Chaudhuri · Jacob Imola · Ashwin Machanavajjhala
Differentially Private Anonymized Histograms	Ananda Theertha Suresh
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate	James Jordon · Jinsung Yoon · Mihaela van der Schaar
Differentially Private Bayesian Linear Regression	Garrett Bernstein · Daniel Sheldon
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy	Jonathan Ullman · Adam Sealfon
Locally Private Gaussian Estimation	Matthew Joseph · Janardhan Kulkarni · Jieming Mao · Steven Wu
Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases	Xiyang Liu · Sewoong Oh
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians	Gautam Kamath · Or Sheffet · Vikrant Singhal · Jonathan Ullman
Partially Encrypted Deep Learning using Functional Encryption	Théo Ryffel · David Pointcheval · Francis Bach · Edouard Dufour-Sans · Romain Gay
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation	Devin Reich · Ariel Todoki · Rafael Dowsley · Martine De Cock · anderson nascimento
Adversarial Training and Robustness for Multiple Perturbations	Florian Tramer · Dan Boneh
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks	Yaqin Zhou · Shangqing Liu · Jingkai Siow · Xiaoning Du · Yang Liu
Differentially Private Distributed Data Summarization under Covariate Shift	Kanthi Sarpatwar · Karthikeyan Shanmugam · Venkata Sitaramagiridharganesh Ganapavarapu · Ashish Jagmohan · Roman Vaculin
Private Hypothesis Selection	Mark Bun · Gautam Kamath · Thomas Steinke · Steven Wu
Facility Location Problem in Differential Privacy Model Revisited	Yunus Esencayi · Marco Gaboardi · Shi Li · Di Wang
KNG: The K-Norm Gradient Mechanism	Matthew Reimherr · Jordan Awan
Locally Private Learning without Interaction Requires Separation	Amit Daniely · Vitaly Feldman
Lower Bounds on Adversarial Robustness from Optimal Transport	Arjun Nitin Bhagoji · Daniel Cullina · Prateek Mittal
On Differentially Private Graph Sparsification and Applications	Raman Arora · Jalaj Upadhyay
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces	Baoxiang Wang · Nidhi Hegde
REM: From Structural Entropy to Community Structure Deception	Yiwei Liu · Jiamou Liu · Zijian Zhang · Liehuang Zhu · Angsheng Li
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks	Lixin Fan · Kam Woh Ng · Chee Seng Chan
SHE: A Fast and Accurate Deep Neural Network for Encrypted Data	Qian Lou · Lei Jiang
Theoretical evidence for adversarial robustness through randomization	Rafael Pinot · Laurent Meunier · Alexandre Araujo · Hisashi Kashima · Florian Yger · Cedric Gouy-Pailler · Jamal Atif
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks	Hadi Salman · Greg Yang · Huan Zhang · Cho-Jui Hsieh · Pengchuan Zhang
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors	Janardhan Kulkarni · Olga Ohrimenko · Bolin Ding · Sergey Yekhanin · Joshua Allen · Harsha Nori
Deep Leakage from Gradients	Ligeng Zhu · Zhijian Liu · Song Han
Defending Neural Backdoors via Generative Distribution Modeling	Ximing Qiao · Yukun Yang · Hai Li
Differential Privacy Has Disparate Impact on Model Accuracy	Eugene Bagdasaryan · Omid Poursaeed · Vitaly Shmatikov
Differentially Private Covariance Estimation	Kareem Amin · Travis Dick · Alex Kulesza · Andres Munoz · Sergei Vassilvitskii
Differentially Private Markov Chain Monte Carlo	Mikko Heikkilä · Joonas Jälkö · Onur Dikmen · Antti Honkela
Elliptical Perturbations for Differential Privacy	Matthew Reimherr · Jordan Awan
Oblivious Sampling Algorithms for Private Data Analysis	Olga Ohrimenko · Sajin Sasy
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition	David Durfee · Ryan Rogers
Privacy Amplification by Mixing and Diffusion Mechanisms	Borja Balle · Gilles Barthe · Marco Gaboardi · Joseph Geumlek
Private Stochastic Convex Optimization with Optimal Rates	Raef Bassily · Vitaly Feldman · Kunal Talwar · Abhradeep Guha Thakurta

Program Understanding and Generation  [Top]

Code Generation as a Dual Task of Code Summarization	Bolin Wei · Ge Li · Xin Xia · Zhiyi Fu · Zhi Jin
Compiler Auto-Vectorization with Imitation Learning	Charith Mendis · Cambridge Yang · Yewen Pu · Dr.Saman Amarasinghe · Michael Carbin
Learning Transferable Graph Exploration	Hanjun Dai · Yujia Li · Chenglong Wang · Rishabh Singh · Po-Sen Huang · Pushmeet Kohli
Neural Attribution for Semantic Bug-Localization in Student Programs	Rahul Gupta · Aditya Kanade · Shirish Shevade
Program Synthesis and Semantic Parsing with Learned Code Idioms	Eui Chul Shin · Miltiadis Allamanis · Marc Brockschmidt · Alex Polozov
SPoC: Search-based Pseudocode to Code	Sumith Kulal · Panupong Pasupat · Kartik Chandra · Mina Lee · Oded Padon · Alex Aiken · Percy Liang
Write, Execute, Assess: Program Synthesis with a REPL	Kevin Ellis · Maxwell Nye · Yewen Pu · Felix Sosa · Josh Tenenbaum · Armando Solar-Lezama

Quantitative Finance and Econometrics  [Top]

Cross-sectional Learning of Extremal Dependence among Financial Assets

Xing Yan · Qi Wu · Wen Zhang

Recommender Systems  [Top]

Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning	Ruiyi Zhang · Tong Yu · Yilin Shen · Hongxia Jin · Changyou Chen
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation	Xueying Bai · Jian Guan · Hongning Wang
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems	Han Zhu · Daqing Chang · Ziru Xu · Pengye Zhang · Xiang Li · Jie He · Han Li · Jian Xu · Kun Gai

Robotics  [Top]

Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations	Andrew Spielberg · Allan Zhao · Yuanming Hu · Tao Du · Wojciech Matusik · Daniela Rus
Multiple Futures Prediction	Charlie Tang · Russ Salakhutdinov
Neural Lyapunov Control	Ya-Chien Chang · Nima Roohi · Sicun Gao
On Single Source Robustness in Deep Fusion Models	Taewan Kim · Joydeep Ghosh
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller	Pratyusha Sharma · Deepak Pathak · Abhinav Gupta

Signal Processing  [Top]

Data-driven Estimation of Sinusoid Frequencies	Gautier Izacard · Sreyas Mohan · Carlos Fernandez-Granda
Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor	Meera Pai · Animesh Kumar
Don't take it lightly: Phasing optical random projections with unknown operators	Sidharth Gupta · Remi Gribonval · Laurent Daudet · Ivan Dokmanić

Sustainability  [Top]

Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network

Jennifer Cardona · Michael Howland · John Dabiri

Time Series Analysis  [Top]

Diffeomorphic Temporal Alignment Nets	Ron A Shapira Weber · Matan Eyal · Nicki Skafte · Oren Shriki · Oren Freifeld
DTWNet: a Dynamic Time Warping Network	Xingyu Cai · Tingyang Xu · Jinfeng Yi · Junzhou Huang · Sanguthevar Rajasekaran
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting	Shiyang Li · Xiaoyong Jin · Yao Xuan · Xiyou Zhou · Wenhu Chen · Yu-Xiang Wang · Xifeng Yan
Fully Neural Network based Model for General Temporal Point Processes	Takahiro Omi · naonori ueda · Kazuyuki Aihara
GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series	Edward De Brouwer · Jaak Simm · Adam Arany · Yves Moreau
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes	David Salinas · Michael Bohlke-Schneider · Laurent Callot · Roberto Medico · Jan Gasthaus
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling	Qitian Wu · Zixuan Zhang · Xiaofeng Gao · Junchi Yan · Guihai Chen
Learning Representations for Time Series Clustering	Qianli Ma · Jiawei Zheng · Sen Li · Gary W Cottrell
Multi-Resolution Weak Supervision for Sequential Data	Paroma Varma · Frederic Sala · Shiori Sagawa · Jason Fries · Daniel Fu · Saelig Khattar · Ashwini Ramamoorthy · Ke Xiao · Kayvon Fatahalian · James Priest · Christopher Ré
Neural Jump Stochastic Differential Equations	Junteng Jia · Austin Benson
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models	Vincent LE GUEN · Nicolas THOME
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting	Rajat Sen · Hsiang-Fu Yu · Inderjit S Dhillon
Unsupervised Scalable Representation Learning for Multivariate Time Series	Jean-Yves Franceschi · Aymeric Dieuleveut · Martin Jaggi

Tracking and Motion in Video  [Top]

muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking	Congchao Wang · Yizhi Wang · Yinxue Wang · Chiung-Ting Wu · Guoqiang Yu
Region-specific Diffeomorphic Metric Mapping	Zhengyang Shen · Francois-Xavier Vialard · Marc Niethammer
Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks	Vineet Kosaraju · Amir Sadeghian · Roberto Martín-Martín · Ian Reid · Hamid Rezatofighi · Silvio Savarese

Video Analysis  [Top]

LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition	Zuxuan Wu · Caiming Xiong · Yu-Gang Jiang · Larry Davis
Recurrent Space-time Graph Neural Networks	Andrei Nicolicioiu · Iulia Duta · Marius Leordeanu

Visual Question Answering  [Top]

Connective Cognition Network for Directional Visual Commonsense Reasoning	Aming Wu · Linchao Zhu · Yahong Han · Yi Yang
Heterogeneous Graph Learning for Visual Commonsense Reasoning	Weijiang Yu · Jingwen Zhou · Weihao Yu · Xiaodan Liang · Nong Xiao
Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning	Wonjae Kim · Yoonho Lee
RUBi: Reducing Unimodal Biases for Visual Question Answering	Remi Cadene · Corentin Dancette · Hedi Ben younes · Matthieu Cord · Devi Parikh
Self-Critical Reasoning for Robust Visual Question Answering	Jialin Wu · Raymond Mooney
Variational Structured Semantic Inference for Diverse Image Captioning	Fuhai Chen · Rongrong Ji · Jiayi Ji · Xiaoshuai Sun · Baochang Zhang · Xuri Ge · Yongjian Wu · Feiyue Huang · Yan Wang
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks	Jiasen Lu · Dhruv Batra · Devi Parikh · Stefan Lee
Visual Concept-Metaconcept Learning	Chi Han · Jiayuan Mao · Chuang Gan · Josh Tenenbaum · Jiajun Wu

Visual Scene Analysis and Interpretation  [Top]

Adaptively Aligned Image Captioning via Adaptive Attention Time	Lun Huang · Wenmin Wang · Yaxian Xia · Jie Chen
Multiview Aggregation for Learning Category-Specific Shape Reconstruction	Srinath Sridhar · Davis Rempe · Julien Valentin · Bouaziz Sofien · Leonidas J Guibas
TAB-VCR: Tags and Attributes based VCR Baselines	Jingxiang Lin · Unnat Jain · Alexander Schwing
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior	Cheng-Chun Hsu · Kuang-Jui Hsu · Chung-Chi Tsai · Yen-Yu Lin · Yung-Yu Chuang

Web Applications and Internet Data  [Top]

iSplit LBI: Individualized Partial Ranking with Ties via Split LBI

Qianqian Xu · Xinwei Sun · Zhiyong Yang · Xiaochun Cao · Qingming Huang · Yuan Yao

Data, Challenges, Implementations, and Software

Benchmarks  [Top]

Detecting Overfitting via Adversarial Examples	Roman Werpachowski · András György · Csaba Szepesvari
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation	Ruibo Tu · Kun Zhang · Bo Bertilson · Hedvig Kjellstrom · Cheng Zhang

Data Sets or Data Repositories  [Top]

Cold Case: The Lost MNIST Digits	Chhavi Yadav · Leon Bottou
The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers	Alex Lu · Amy Lu · Wiebke Schormann · Marzyeh Ghassemi · David Andrews · Alan Moses
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models	Andrei Barbu · David Mayo · Julian Alverio · William Luo · Christopher Wang · Dan Gutfreund · Josh Tenenbaum · Boris Katz
Park: An Open Platform for Learning-Augmented Computer Systems	Hongzi Mao · Parimarjan Negi · Akshay Narayan · Hanrui Wang · Jiacheng Yang · Haonan Wang · Ryan Marcus · ravichandra addanki · Mehrdad Khani Shirkoohi · Songtao He · Vikram Nathan · Frank Cangialosi · Shaileshh Venkatakrishnan · Wei-Hung Weng · Song Han · Tim Kraska · Dr.Mohammad Alizadeh
STREETS: A Novel Camera Network Dataset for Traffic Flow	Corey Snyder · Minh Do

Software Toolkits  [Top]

A Step Toward Quantifying Independently Reproducible Machine Learning Research	Edward Raff
GENO -- GENeric Optimization for Classical Machine Learning	Soeren Laue · Matthias Mitterreiter · Joachim Giesen
PyTorch: An Imperative Style, High-Performance Deep Learning Library	Adam Paszke · Sam Gross · Francisco Massa · Adam Lerer · James Bradbury · Gregory Chanan · Trevor Killeen · Zeming Lin · Natalia Gimelshein · Luca Antiga · Alban Desmaison · Andreas Kopf · Edward Yang · Zachary DeVito · Martin Raison · Alykhan Tejani · Sasank Chilamkurthy · Benoit Steiner · Lu Fang · Junjie Bai · Soumith Chintala

Virtual Environments  [Top]

PHYRE: A New Benchmark for Physical Reasoning

Anton Bakhtin · Laurens van der Maaten · Justin Johnson · Laura Gustafson · Ross Girshick

Deep Learning

Adversarial Networks  [Top]

Convergence of Adversarial Training in Overparametrized Neural Networks	Ruiqi Gao · Tianle Cai · Haochuan Li · Cho-Jui Hsieh · Liwei Wang · Jason Lee
Discriminator optimal transport	Akinori Tanaka
Learning from Label Proportions with Generative Adversarial Networks	Jiabin Liu · Bo Wang · Zhiquan Qi · YingJie Tian · Yong Shi
Learning GANs and Ensembles Using Discrepancy	Ben Adlam · Corinna Cortes · Mehryar Mohri · Ningshan Zhang
MarginGAN: Adversarial Training in Semi-Supervised Learning	Jinhao Dong · Tong Lin
Modeling Tabular data using Conditional GAN	Lei Xu · Maria Skoularidou · Alfredo Cuesta-Infante · Kalyan Veeramachaneni
Beyond the Single Neuron Convex Barrier for Neural Network Certification	Gagandeep Singh · Rupanshu Ganvir · Markus Püschel · Martin Vechev
Quality Aware Generative Adversarial Networks	KANCHARLA PARIMALA · Sumohana Channappayya
Random deep neural networks are biased towards simple functions	Giacomo De Palma · Bobak Kiani · Seth Lloyd
Reducing Noise in GAN Training with Variance Reduced Extragradient	Tatjana Chavdarova · Gauthier Gidel · François Fleuret · Simon Lacoste-Julien
Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game	Ngoc-Trung Tran · Viet-Hung Tran · Bao-Ngoc Nguyen · Linxiao Yang · Ngai-Man (Man) Cheung
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection	Vladimir V. Kniaz · Vladimir Knyaz · Fabio Remondino
Training Language GANs from Scratch	Cyprien de Masson d'Autume · Shakir Mohamed · Mihaela Rosca · Jack Rae
Zero-shot Knowledge Transfer via Adversarial Belief Matching	Paul Micaelli · Amos Storkey

Attention Models  [Top]

Are Sixteen Heads Really Better than One?	Paul Michel · Omer Levy · Graham Neubig
Compositional De-Attention Networks	Yi Tay · Anh Tuan Luu · Aston Zhang · Shuohang Wang · Siu Cheung Hui
Geometry-Aware Neural Rendering	Joshua Tobin · Wojciech Zaremba · Pieter Abbeel
Image Captioning: Transforming Objects into Words	Simao Herdade · Armin Kappeler · Kofi Boakye · Joao Soares
Learning by Abstraction: The Neural State Machine	Drew Hudson · Christopher Manning
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time	Karlis Freivalds · Emīls Ozoliņš · Agris Šostaks
Novel positional encodings to enable tree-based transformers	Vighnesh Shiv · Chris Quirk
Self-attention with Functional Time Representation Learning	Da Xu · Chuanwei Ruan · Evren Korpeoglu · Sushant Kumar · Kannan Achan
Understanding Attention and Generalization in Graph Neural Networks	Boris Knyazev · Graham W Taylor · Mohamed Amer

Biologically Plausible Deep Networks  [Top]

Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks	Dina Obeid · Hugo Ramambason · Cengiz Pehlevan
Deep Learning without Weight Transport	Mohamed Akrout · Collin Wilson · Peter Humphreys · Timothy Lillicrap · Douglas Tweed
Neural networks grown and self-organized by noise	Guruprasad Raghavan · Matt Thomson
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks	Wenrui Zhang · Peng Li
Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks	Hosein Hasani · Mahdieh Soleymani · Hamid Aghajan
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input	Maxence Ernoult · Benjamin Scellier · Yoshua Bengio · Damien Querlioz · Julie Grollier

CNN Architectures  [Top]

A General Theory of Equivariant CNNs on Homogeneous Spaces	Taco S Cohen · Mario Geiger · Maurice Weiler
Abstraction based Output Range Analysis for Neural Networks	Pavithra Prabhakar · Zahra Rahimi Afzal
ANODEV2: A Coupled Neural ODE Framework	Tianjun Zhang · Zhewei Yao · Amir Gholami · Joseph Gonzalez · Kurt Keutzer · Michael W Mahoney · George Biros
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction	Hao Zheng · Faming Fang · Guixu Zhang
CNN^{2}: Viewpoint Generalization via a Binocular Vision	Wei-Da Chen · Shan-Hung (Brandon) Wu
CondConv: Conditionally Parameterized Convolutions for Efficient Inference	Brandon Yang · Gabriel Bender · Quoc V Le · Jiquan Ngiam
Convolution with even-sized kernels and symmetric padding	Shuang Wu · Guanrui Wang · Pei Tang · Feng Chen · Luping Shi
Deep Active Learning with a Neural Architecture Search	Yonatan Geifman · Ran El-Yaniv
Deep Scale-spaces: Equivariance Over Scale	Daniel Worrall · Max Welling
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters	W. O. K. Asiri Suranga Wijesinghe · Qing Wang
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks	Kohei Hayashi · Taiki Yamaguchi · Yohei Sugawara · Shin-ichi Maeda
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias	Stéphane d'Ascoli · Levent Sagun · Giulio Biroli · Joan Bruna
Fixing the train-test resolution discrepancy	Hugo Touvron · Andrea Vedaldi · Matthijs Douze · Herve Jegou
Gaussian-Based Pooling for Convolutional Neural Networks	Takumi Kobayashi
General E(2)-Equivariant Steerable CNNs	Maurice Weiler · Gabriele Cesa
Learning Stable Deep Dynamics Models	J. Zico Kolter · Gaurav Manek
Neural Similarity Learning	Weiyang Liu · Zhen Liu · James M Rehg · Le Song
Cross-channel Communication Networks	Jianwei Yang · Zhile Ren · Chuang Gan · Hongyuan Zhu · Devi Parikh
Positional Normalization	Boyi Li · Felix Wu · Kilian Weinberger · Serge Belongie
Powerset Convolutional Neural Networks	Chris Wendler · Markus Püschel · Dan Alistarh
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks	Qiyang Li · Saminul Haque · Cem Anil · James Lucas · Roger Grosse · Joern-Henrik Jacobsen
Self-Routing Capsule Networks	Taeyoung Hahn · Myeongjang Pyeon · Gunhee Kim
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.	Sawyer Birnbaum · Volodymyr Kuleshov · Zayd Enam · Pang Wei Koh · Stefano Ermon

Deep Autoencoders  [Top]

AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling	Bichuan Guo · Yuxing Han · Jiangtao Wen
Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes	Gunpil Hwang · Seohyeon Kim · Hyeon-Min Bae
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders	Emile Mathieu · Charline Le Lan · Chris J. Maddison · Ryota Tomioka · Yee Whye Teh

Efficient Inference Methods  [Top]

Channel Gating Neural Networks	Weizhe Hua · Yuan Zhou · Christopher De Sa · Zhiru Zhang · G. Edward Suh
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask	Hattie Zhou · Janice Lan · Rosanne Liu · Jason Yosinski
Point-Voxel CNN for Efficient 3D Deep Learning	Zhijian Liu · Haotian Tang · Yujun Lin · Song Han
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks	Zhonghui You · Kun Yan · Jinmian Ye · Meng Ma · Ping Wang
Inherent Weight Normalization in Stochastic Neural Networks	Georgios Detorakis · Sourav Dutta · Abhishek Khanna · Matthew Jerry · Suman Datta · Emre Neftci
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization	Shangyu Chen · Wenya Wang · Sinno Jialin Pan
Model Compression with Adversarial Robustness: A Unified Optimization Framework	Shupeng Gui · Haotao N Wang · Haichuan Yang · Chen Yu · Zhangyang Wang · Ji Liu
Positive-Unlabeled Compression on the Cloud	Yixing Xu · Yunhe Wang · Hanting Chen · Kai Han · Chunjing XU · Dacheng Tao · Chang Xu
Combining Generative and Discriminative Models for Hybrid Inference	Victor Garcia Satorras · Max Welling · Zeynep Akata
Deep Model Transferability from Attribution Maps	Jie Song · Yixin Chen · Xinchao Wang · Chengchao Shen · Mingli Song
Focused Quantization for Sparse CNNs	Yiren Zhao · Xitong Gao · Daniel Bates · Robert Mullins · Cheng-Zhong Xu
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks	Xiaohan Ding · guiguang ding · Xiangxin Zhou · Yuchen Guo · Jungong Han · Ji Liu
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization	Koen Helwegen · James Widdicombe · Lukas Geiger · Zechun Liu · Kwang-Ting Cheng · Roeland Nusselder
Normalization Helps Training of Quantized LSTM	Lu Hou · Jinhua Zhu · James Kwok · Fei Gao · Tao Qin · Tie-Yan Liu
Post training 4-bit quantization of convolutional networks for rapid-deployment	Ron Banner · Yury Nahshan · Daniel Soudry
SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models	Linfeng Zhang · Zhanhong Tan · Jiebo Song · Jingwei Chen · Chenglong Bao · Kaisheng Ma
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices	Don Dennis · Durmus Alp Emre Acar · Vikram Mandikal · Vinu Sankar Sadasivan · Venkatesh Saligrama · Harsha Vardhan Simhadri · Prateek Jain

Efficient Training Methods  [Top]

A Fourier Perspective on Model Robustness in Computer Vision	Dong Yin · Raphael Gontijo Lopes · Jon Shlens · Ekin Dogus Cubuk · Justin Gilmer
A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation	Mitsuru Kusumoto · Takuya Inoue · Gentaro Watanabe · Takuya Akiba · Masanori Koyama
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off	Yaniv Blumenfeld · Dar Gilboa · Daniel Soudry
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks	Jiong Zhang · Hsiang-Fu Yu · Inderjit S Dhillon
Backprop with Approximate Activations for Memory-efficient Network Training	Ayan Chakrabarti · Benjamin Moseley
Bridging Machine Learning and Logical Reasoning by Abductive Learning	Wang-Zhou Dai · Qiuling Xu · Yang Yu · Zhi-Hua Zhou
E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy	Ziyu Jiang · Yue Wang · Xiaohan Chen · Pengfei Xu · Yang Zhao · Yingyan Lin · Zhangyang Wang
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks	Xiao Sun · Jungwook Choi · Chia-Yu Chen · Naigang Wang · Swagath Venkataramani · Vijayalakshmi (Viji) Srinivasan · Xiaodong Cui · Wei Zhang · Kailash Gopalakrishnan
Initialization of ReLUs for Dynamical Isometry	Rebekka Burkholz · Alina Dubatovka
Invert to Learn to Invert	Patrick Putzky · Max Welling
Learning Data Manipulation for Augmentation and Weighting	Zhiting Hu · Bowen Tan · Russ Salakhutdinov · Tom Mitchell · Eric Xing
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences	Ehsan Amid · Manfred K. Warmuth · Rohan Anil · Tomer Koren
When does label smoothing help?	Rafael Müller · Simon Kornblith · Geoffrey E Hinton

Embedding Approaches  [Top]

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces	Chuan Guo · Ali Mousavi · Xiang Wu · Daniel Holtmann-Rice · Satyen Kale · Sashank Reddi · Sanjiv Kumar
End to end learning and optimization on graphs	Bryan Wilder · Eric Ewing · Bistra Dilkina · Milind Tambe
On the Downstream Performance of Compressed Word Embeddings	Avner May · Jian Zhang · Tri Dao · Christopher Ré
Quantum Embedding of Knowledge for Reasoning	Dinesh Garg · Shajith Ikbal Mohamed · Santosh K. Srivastava · Harit Vishwakarma · Hima Karanam · L Venkata Subramaniam
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space	Bjarne Sievers · Jonathan Sauder
Embedding Symbolic Knowledge into Deep Networks	Xie Yaqi · Ziwei Xu · Kuldeep S Meel · Mohan Kankanhalli · Harold Soh
Spherical Text Embedding	Yu Meng · Jiaxin Huang · Guangyuan Wang · Chao Zhang · Honglei Zhuang · Lance Kaplan · Jiawei Han
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers	Liwei Wu · Shuqing Li · Cho-Jui Hsieh · James Sharpnack

Generative Models  [Top]

A Primal-Dual link between GANs and Autoencoders	Hisham Husain · Richard Nock · Robert Williamson
A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models	Maxim Kuznetsov · Daniil Polykovskiy · Dmitry Vetrov · Alex Zhebrak
Adversarial Self-Defense for Cycle-Consistent GANs	Dina Bashkirova · Ben Usman · Kate Saenko
Controllable Text-to-Image Generation	Bowen Li · Xiaojuan Qi · Thomas Lukasiewicz · Philip Torr
Dancing to Music	Hsin-Ying Lee · Xiaodong Yang · Ming-Yu Liu · Ting-Chun Wang · Yu-Ding Lu · Ming-Hsuan Yang · Jan Kautz
DppNet: Approximating Determinantal Point Processes with Deep Networks	Zelda Mariet · Yaniv Ovadia · Jasper Snoek
Efficient Graph Generation with Graph Recurrent Attention Networks	Renjie Liao · Yujia Li · Yang Song · Shenlong Wang · Will Hamilton · David Duvenaud · Raquel Urtasun · Richard Zemel
Explicit Disentanglement of Appearance and Perspective in Generative Models	Nicki Skafte · Søren Hauberg
Flow-based Image-to-Image Translation with Feature Disentanglement	Ruho Kondo · Keisuke Kawano · Satoshi Koide · Takuro Kutsuna
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection	Bingzhe Wu · Shiwan Zhao · Chaochao Chen · Haoyang Xu · Li Wang · Xiaolu Zhang · Guangyu Sun · Jun Zhou
Improved Precision and Recall Metric for Assessing Generative Models	Tuomas Kynkäänniemi · Tero Karras · Samuli Laine · Jaakko Lehtinen · Timo Aila
Knowledge Extraction with No Observable Data	Jaemin Yoo · Minyong Cho · Taebum Kim · U Kang
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge	Tingting Qiao · Jing Zhang · Duanqing Xu · Dacheng Tao
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph	Yikang LI · Tao Ma · Yeqi Bai · Nan Duan · Sining Wei · Xiaogang Wang
Sequential Neural Processes	Gautam Singh · Jaesik Yoon · Youngsung Son · Sungjin Ahn
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction	Yunji Kim · Seonghyeon Nam · In Cho · Seon Joo Kim
Adaptive Density Estimation for Generative Models	Thomas Lucas · Konstantin Shmelkov · Karteek Alahari · Cordelia Schmid · Jakob Verbeek
Adversarial Fisher Vectors for Unsupervised Representation Learning	Joshua Susskind · Shuangfei Zhai · Walter Talbott · Carlos Guestrin
Co-Generation with GANs using AIS based HMC	Tiantian Fang · Alexander Schwing
Compression with Flows via Local Bits-Back Coding	Jonathan Ho · Evan Lohn · Pieter Abbeel
Direct Optimization through \arg \max for Discrete Variational Auto-Encoder	Guy Lorberbom · Tommi Jaakkola · Andreea Gane · Tamir Hazan
Fast and Provable ADMM for Learning with Generative Priors	Fabian Latorre · Armin eftekhari · Volkan Cevher
Generative Modeling by Estimating Gradients of the Data Distribution	Yang Song · Stefano Ermon
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models	Sharon Zhou · Mitchell Gordon · Ranjay Krishna · Austin Narcomey · Li Fei-Fei · Michael Bernstein
Implicit Generation and Modeling with Energy Based Models	Yilun Du · Igor Mordatch
Invertible Convolutional Flow	Mahdi Karami · Dale Schuurmans · Jascha Sohl-Dickstein · Laurent Dinh · Daniel Duckworth
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series	Yulia Rubanova · Tian Qi Chen · David Duvenaud
MaCow: Masked Convolutional Generative Flow	Xuezhe Ma · Xiang Kong · Shanghang Zhang · Eduard Hovy
Mining GOLD Samples for Conditional GANs	Sangwoo Mo · Chiheon Kim · Sungwoong Kim · Minsu Cho · Jinwoo Shin
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model	Erik Nijkamp · Mitch Hill · Song-Chun Zhu · Ying Nian Wu
Residual Flows for Invertible Generative Modeling	Tian Qi Chen · Jens Behrmann · David Duvenaud · Joern-Henrik Jacobsen
Time-series Generative Adversarial Networks	Jinsung Yoon · Daniel Jarrett · M Van Der Schaar
Twin Auxilary Classifiers GAN	Mingming Gong · Yanwu Xu · Chunyuan Li · Kun Zhang · Kayhan Batmanghelich
Deep Generative Video Compression	Salvator Lombardo · JUN HAN · Christopher Schroers · Stephan Mandt
A Model to Search for Synthesizable Molecules	John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling	Lars Maaløe · Marco Fraccaro · Valentin Liévin · Ole Winther
Classification Accuracy Score for Conditional Generative Models	Suman Ravuri · Oriol Vinyals
Discrete Flows: Invertible Generative Models of Discrete Data	Dustin Tran · Keyon Vafa · Kumar Agrawal · Laurent Dinh · Ben Poole
First Order Motion Model for Image Animation	Aliaksandr Siarohin · Stéphane Lathuillère · Sergey Tulyakov · Elisa Ricci · Nicu Sebe
G2SAT: Learning to Generate SAT Formulas	Jiaxuan You · Haoze Wu · Clark Barrett · Raghuram Ramanujan · Jure Leskovec
Multi-objects Generation with Amortized Structural Regularization	Taufik Xu · Chongxuan LI · Jun Zhu · Bo Zhang
Neural Multisensory Scene Inference	Jae Hyun Lim · Pedro O. Pinheiro · Negar Rostamzadeh · Chris Pal · Sungjin Ahn
Neural Spline Flows	Conor Durkan · Artur Bekasov · Iain Murray · George Papamakarios
Progressive Augmentation of GANs	Dan Zhang · Anna Khoreva
Quantum Wasserstein Generative Adversarial Networks	Shouvanik Chakrabarti · Huang Yiming · Tongyang Li · Soheil Feizi · Xiaodi Wu
Energy-Inspired Models: Learning with Sampler-Induced Distributions	John Lawson · George Tucker · Bo Dai · Rajesh Ranganath
Sequence Modeling with Unconstrained Generation Order	Dmitrii Emelianenko · Elena Voita · Pavel Serdyukov
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules	Niklas Gebauer · Michael Gastegger · Kristof Schütt
Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse	James Lucas · George Tucker · Roger Grosse · Mohammad Norouzi
Unsupervised Learning of Object Keypoints for Perception and Control	Tejas Kulkarni · Ankush Gupta · Catalin Ionescu · Sebastian Borgeaud · Malcolm Reynolds · Andrew Zisserman · Volodymyr Mnih
A Domain Agnostic Measure for Monitoring and Evaluating GANs	Paulina Grnarova · Kfir Y. Levy · Aurelien Lucchi · Nathanael Perraudin · Ian Goodfellow · Thomas Hofmann · Andreas Krause
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting	Aditya Grover · Jiaming Song · Ashish Kapoor · Kenneth Tran · Alekh Agarwal · Eric Horvitz · Stefano Ermon
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders	Natasa Tagasovska · Damien Ackerer · Thibault Vatter
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data	Gabriel Loaiza-Ganem · Sean Perkins · Karen Schroeder · Mark Churchland · John Cunningham
Discrete Object Generation with Reversible Inductive Construction	Ari Seff · Wenda Zhou · Farhan Damani · Abigail Doyle · Ryan Adams
Generating Diverse High-Fidelity Images with VQ-VAE-2	Ali Razavi · Aaron van den Oord · Oriol Vinyals
Generative Well-intentioned Networks	Justin Cosentino · Jun Zhu
Graph Normalizing Flows	Jenny Liu · Aviral Kumar &mmiddot; Jimmy Ba · Jamie Kiros · Kevin Swersky
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model	Wenbo Gong · Sebastian Tschiatschek · Sebastian Nowozin · Richard E Turner · José Miguel Hernández-Lobato · Cheng Zhang
Integer Discrete Flows and Lossless Compression	Emiel Hoogeboom · Jorn Peters · Rianne van den Berg · Max Welling
Amortized Bethe Free Energy Minimization for Learning MRFs	Sam Wiseman · Yoon Kim
MintNet: Building Invertible Neural Networks with Masked Convolutions	Yang Song · Chenlin Meng · Stefano Ermon
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation	Yukai Liu · Rose Yu · Stephan Zheng · Eric Zhan · Yisong Yue
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks	Cagatay Yildiz · Markus Heinonen · Harri Lahdesmaki
Scalable Deep Generative Relational Model with High-Order Node Dependence	Xuhui Fan · Bin Li · Caoyuan Li · Scott SIsson · Ling Chen
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models	Yuge Shi · Siddharth N · Brooks Paige · Philip Torr
Variational Temporal Abstraction	Taesup Kim · Sungjin Ahn · Yoshua Bengio

Interaction-Based Deep Networks  [Top]

GNNExplainer: Generating Explanations for Graph Neural Networks

Zhitao Ying · Dylan Bourgeois · Jiaxuan You · Marinka Zitnik · Jure Leskovec

Memory-Augmented Neural Networks  [Top]

Episodic Memory in Lifelong Language Learning	Cyprien de Masson d'Autume · Sebastian Ruder · Lingpeng Kong · Dani Yogatama
Generalization of Reinforcement Learners with Working and Episodic Memory	Meire Fortunato · Melissa Tan · Ryan Faulkner · Steven Hansen · Adrià Puigdomènech Badia · Gavin Buttimore · Charles Deck · Joel Leibo · Charles Blundell
Large Memory Layers with Product Keys	Guillaume Lample · Alexandre Sablayrolles · Marc'Aurelio Ranzato · Ludovic Denoyer · Herve Jegou
Ordered Memory	Yikang Shen · Shawn Tan · Arian Hosseini · Zhouhan Lin · Alessandro Sordoni · Aaron Courville

Optimization for Deep Networks  [Top]

An Improved Analysis of Training Over-parameterized Deep Neural Networks	Difan Zou · Quanquan Gu
Controlling Neural Level Sets	Matan Atzmon · Niv Haim · Lior Yariv · Ofer Israelov · Haggai Maron · Yaron Lipman
Deep Equilibrium Models	Shaojie Bai · J. Zico Kolter · Vladlen Koltun
Differentiable Cloth Simulation for Inverse Problems	Junbang Liang · Ming Lin · Vladlen Koltun
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks	Mahyar Fazlyab · Alexander Robey · Hamed Hassani · Manfred Morari · George Pappas
Fine-grained Optimization of Deep Neural Networks	Mete Ozay
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks	Yuan Cao · Quanquan Gu
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective	Lili Su · Pengkun Yang
Stagewise Training Accelerates Convergence of Testing Error Over SGD	Zhuoning Yuan · Yan Yan · Rong Jin · Tianbao Yang
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks	Yuanzhi Li · Colin Wei · Tengyu Ma
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle	Dinghuai Zhang · Tianyuan Zhang · Yiping Lu · Zhanxing Zhu · Bin Dong
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration	Florian Scheidegger · Luca Benini · Costas Bekas · A. Cristiano I. Malossi
Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks	Gauthier Gidel · Francis Bach · Simon Lacoste-Julien
In-Place Zero-Space Memory Protection for CNN	Hui Guan · Lin Ning · Zhen Lin · Xipeng Shen · Huiyang Zhou · Seung-Hwan Lim
Large Scale Structure of Neural Network Loss Landscapes	Stanislav Fort · Stanislaw Jastrzebski
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers	Zeyuan Allen-Zhu · Yuanzhi Li · Yingyu Liang
Limitations of the empirical Fisher approximation for natural gradient descent	Frederik Kunstner · Philipp Hennig · Lukas Balles
Maximum Mean Discrepancy Gradient Flow	Michael Arbel · Anna Korba · Adil SALIM · Arthur Gretton
On Lazy Training in Differentiable Programming	Lénaïc Chizat · Edouard Oyallon · Francis Bach
Reducing the variance in online optimization by transporting past gradients	Sébastien Arnold · Pierre-Antoine Manzagol · Reza Babanezhad Harikandeh · Ioannis Mitliagkas · Nicolas Le Roux
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks	Yuan Cao · Quanquan Gu
Understanding and Improving Layer Normalization	Jingjing Xu · Xu Sun · Zhiyuan Zhang · Guangxiang Zhao · Junyang Lin
LCA: Loss Change Allocation for Neural Network Training	Janice Lan · Rosanne Liu · Hattie Zhou · Jason Yosinski
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets	Rohith Kuditipudi · Xiang Wang · Holden Lee · Yi Zhang · Zhiyuan Li · Wei Hu · Rong Ge · Sanjeev Arora
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models	Yunfei Teng · Wenbo Gao · François Chalus · Anna Choromanska · Donald Goldfarb · Adrian Weller
Learning Neural Networks with Adaptive Regularization	Han Zhao · Yao-Hung Hubert Tsai · Russ Salakhutdinov · Geoffrey Gordon
Memory Efficient Adaptive Optimization	Rohan Anil · Vineet Gupta · Tomer Koren · Yoram Singer
On the Convergence Rate of Training Recurrent Neural Networks	Zeyuan Allen-Zhu · Yuanzhi Li · Zhao Song
SGD on Neural Networks Learns Functions of Increasing Complexity	Dimitris Kalimeris · Gal Kaplun · Preetum Nakkiran · Benjamin Edelman · Tristan Yang · Boaz Barak · Haofeng Zhang
Towards Understanding the Importance of Shortcut Connections in Residual Networks	Tianyi Liu · Minshuo Chen · Mo Zhou · Simon Du · Enlu Zhou · Tuo Zhao
Trivializations for Gradient-Based Optimization on Manifolds	Mario Lezcano Casado
Using Statistics to Automate Stochastic Optimization	Hunter Lang · Lin Xiao · Pengchuan Zhang
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model	Guodong Zhang · Lala Li · Zachary Nado · James Martens · Sushant Sachdeva · George Dahl · Chris Shallue · Roger Grosse
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent	Jaehoon Lee · Lechao Xiao · Samuel Schoenholz · Yasaman Bahri · Roman Novak · Jascha Sohl-Dickstein · Jeffrey Pennington
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks	Spencer Frei · Yuan Cao · Quanquan Gu
Are deep ResNets provably better than linear predictors?	Chulhee Yun · Suvrit Sra · Ali Jadbabaie
Efficient Rematerialization for Deep Networks	Ravi Kumar · Manish Purohit · Zoya Svitkina · Erik Vee · Joshua Wang
Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks	Guodong Zhang · James Martens · Roger Grosse
How to Initialize your Network? Robust Initialization for WeightNorm & ResNets	Devansh Arpit · Víctor Campos · Yoshua Bengio
Lookahead Optimizer: k steps forward, 1 step back	Michael Zhang · James Lucas · Jimmy Ba · Geoffrey E Hinton
Global Convergence of Gradient Descent for Deep Linear Residual Networks	Lei Wu · Qingcan Wang · Chao Ma
Piecewise Strong Convexity of Neural Networks	Tristan Milne
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization	Thijs Vogels · Sai Praneeth Karimireddy · Martin Jaggi
A Primal Dual Formulation For Deep Learning With Constraints	Yatin Nandwani · Abhishek Pathak · Mausam · Parag Singla
Surfing: Iterative Optimization Over Incrementally Trained Deep Networks	Ganlin Song · Zhou Fan · John Lafferty
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning	Igor Colin · Ludovic DOS SANTOS · Kevin Scaman

Predictive Models  [Top]

A Simple Baseline for Bayesian Uncertainty in Deep Learning	Wesley J Maddox · Pavel Izmailov · Timur Garipov · Dmitry Vetrov · Andrew Gordon Wilson
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs	Ali Sadeghian · Mohammadreza Armandpour · Patrick Ding · Daisy Zhe Wang
High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks	Ruben Villegas · Arkanath Pathak · Harini Kannan · Dumitru Erhan · Quoc V Le · Honglak Lee
Unsupervised learning of object structure and dynamics from videos	Matthias Minderer · Chen Sun · Ruben Villegas · Forrester Cole · Kevin Murphy · Honglak Lee

Recurrent Networks  [Top]

Can SGD Learn Recurrent Neural Networks with Provable Generalization?	Zeyuan Allen-Zhu · Yuanzhi Li
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks	Aya Abdelsalam Ismail · Mohamed Gunady · Luiz Pessoa · Hector Corrada Bravo · Soheil Feizi
Input-Output Equivalence of Unitary and Contractive RNNs	Melikasadat Emami · Mojtaba Sahraee Ardakan · Sundeep Rangan · Alyson Fletcher
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods	Kevin Liang · Guoyin Wang · Yitong Li · Ricardo Henao · Lawrence Carin
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks	Aaron Voelker · Ivana Kajić · Chris Eliasmith
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics	Giancarlo Kerg · Kyle Goyette · Maximilian Puelma Touzel · Gauthier Gidel · Eugene Vorontsov · Yoshua Bengio · Guillaume Lajoie
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics	Niru Maheswaranathan · Alex Williams · Matthew Golub · Surya Ganguli · David Sussillo
Root Mean Square Layer Normalization	Biao Zhang · Rico Sennrich
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets	Joshua Hanson · Maxim Raginsky

Supervised Deep Networks  [Top]

Combinatorial Inference against Label Noise	Paul Hongsuck Seo · Geeho Kim · Bohyung Han
Deep Signature Transforms	Patrick Kidger · Patric Bonnier · Imanol Perez Arribas · Cristopher Salvi · Terry Lyons
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum	Shreyas Saxena · Oncel Tuzel · Dennis DeCoste
Implicit Semantic Data Augmentation for Deep Networks	Yulin Wang · Xuran Pan · Shiji Song · Hong Zhang · Gao Huang · Cheng Wu
Is Deeper Better only when Shallow is Good?	Eran Malach · Shai Shalev-Shwartz
No-Press Diplomacy: Modeling Multi-Agent Gameplay	Philip Paquette · Yuchen Lu · SETON STEVEN BOCCO · Max Smith · Satya O.-G. · Jonathan K. Kummerfeld · Joelle Pineau · Satinder Singh · Aaron Courville
Riemannian batch normalization for SPD neural networks	Daniel Brooks · Olivier Schwander · Frederic Barbaresco · Jean-Yves Schneider · Matthieu Cord

Visualization or Exposition Techniques for Deep Networks  [Top]

A Benchmark for Interpretability Methods in Deep Neural Networks	Sara Hooker · Dumitru Erhan · Pieter-Jan Kindermans · Been Kim
Accurate, reliable and fast robustness evaluation	Wieland Brendel · Jonas Rauber · Matthias Kümmerer · Ivan Ustyuzhaninov · Matthias Bethge
Approximate Feature Collisions in Neural Nets	Ke Li · Tianhao Zhang · Jitendra Malik
Computing Linear Restrictions of Neural Networks	Matthew Sotoudeh · Aditya V Thakur
CXPlain: Causal Explanations for Model Interpretation under Uncertainty	Patrick Schwab · Walter Karlen
Deliberative Explanations: visualizing network insecurities	Pei Wang · Nuno Nvasconcelos
Explanations can be manipulated and geometry is to blame	Ann-Kathrin Dombrowski · Maximillian Alber · Christopher Anders · Marcel Ackermann · Klaus-Robert Müller · Pan Kessel
Fooling Neural Network Interpretations via Adversarial Model Manipulation	Juyeon Heo · Sunghwan Joo · Taesup Moon
Full-Gradient Representation for Neural Network Visualization	Suraj Srinivas · François Fleuret
Grid Saliency for Context Explanations of Semantic Segmentation	Lukas Hoyer · Mauricio Munoz · Prateek Katiyar · Anna Khoreva · Volker Fischer
Intrinsic dimension of data representations in deep neural networks	Alessio Ansuini · Alessandro Laio · Jakob H Macke · Davide Zoccolan
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers	Ari Morcos · Haonan Yu · Michela Paganini · Yuandong Tian
The Geometry of Deep Networks: Power Diagram Subdivision	Randall Balestriero · Romain Cosentino · Behnaam Aazhang · Richard Baraniuk
Visualizing and Measuring the Geometry of BERT	Emily Reif · Ann Yuan · Martin Wattenberg · Fernanda B Viegas · Andy Coenen · Adam Pearce · Been Kim
Visualizing the PHATE of Neural Networks	Scott Gigante · Adam S Charles · Smita Krishnaswamy · Gal Mishne

Neuroscience and Cognitive Science

Brain Imaging  [Top]

A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI	Tao Tu · John Paisley · Stefan Haufe · Paul Sajda
Manifold-regression to predict from MEG/EEG brain signals without source modeling	David Sabbagh · Pierre Ablin · Gael Varoquaux · Alexandre Gramfort · Denis A. Engemann

Brain Mapping  [Top]

Inducing brain-relevant bias in natural language processing models

Dan Schwartz · Mariya Toneva · Leila Wehbe

Brain--Computer Interfaces and Neural Prostheses  [Top]

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces	Benyamin Allahgholizadeh Haghi · Spencer Kellis · Sahil Shah · Maitreyi Ashok · Luke Bashford · Daniel Kramer · Brian Lee · Charles Liu · Richard Andersen · Azita Emami
Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces	Yu Qi · Bin Liu · Yueming Wang · Gang Pan
Efficient characterization of electrically evoked responses for neural interfaces	Nishal Shah · Sasidhar Madugula · Pawel Hottowy · Alexander Sher · Alan Litke · Liam Paninski · E.J. Chichilnisky
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data	Mohammad Reza Keshtkaran · Chethan Pandarinath

Cognitive Science  [Top]

A Bayesian Theory of Conformity in Collective Decision Making	Koosha Khalvati · Saghar Mirbagheri · Seongmin A. Park · Jean-Claude Dreher · Rajesh PN Rao
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations	Kevin Smith · Lingjie Mei · Shunyu Yao · Jiajun Wu · Elizabeth Spelke · Josh Tenenbaum · Tomer Ullman
Compositional generalization through meta sequence-to-sequence learning	Brenden Lake
Universality and individuality in neural dynamics across large populations of recurrent networks	Niru Maheswaranathan · Alex Williams · Matthew Golub · Surya Ganguli · David Sussillo

Connectomics  [Top]

Learning Macroscopic Brain Connectomes via Group-Sparse Factorization

Farzane Aminmansour · Andrew Patterson · Lei Le · Yisu Peng · Daniel Mitchell · Franco Pestilli · Cesar Caiafa · Russell Greiner · Martha White

Human or Animal Learning  [Top]

Coordinated hippocampal-entorhinal replay as structural inference	Talfan Evans · Neil Burgess
Disentangled behavioural representations	Amir Dezfouli · Hassan Ashtiani · Omar Ghattas · Richard Nock · Peter Dayan · Cheng Soon Ong
Teaching Multiple Concepts to a Forgetful Learner	Anette Hunziker · Yuxin Chen · Oisin Mac Aodha · Manuel Gomez Rodriguez · Andreas Krause · Pietro Perona · Yisong Yue · Adish Singla

Language for Cognitive Science  [Top]

Anti-efficient encoding in emergent communication

Rahma Chaabouni · Eugene Kharitonov · Emmanuel Dupoux · Marco Baroni

Memory  [Top]

Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently

Xiao Liu · Xiaolong Zou · Zilong Ji · Gengshuo Tian · Yuanyuan Mi · Tiejun Huang · K. Y. Michael Wong · Si Wu

Neural Coding  [Top]

A neurally plausible model for online recognition and postdiction in a dynamical environment	Li Kevin Wenliang · Maneesh Sahani
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits	Wenhao Zhang · Si Wu · Brent Doiron · Tai Sing Lee
A unified theory for the origin of grid cells through the lens of pattern formation	Ben Sorscher · Gabriel Mel · Surya Ganguli · Samuel Ocko
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse	Cornelius Schröder · Ben James · Leon Lagnado · Philipp Berens
Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes	Rishidev Chaudhuri · Ila Fiete
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex	Jianghong Shi · Eric Shea-Brown · Michael Buice
Flexible information routing in neural populations through stochastic comodulation	Caroline Haimerl · Cristina Savin · Eero Simoncelli
Nonlinear scaling of resource allocation in sensory bottlenecks	Laura Rose Edmondson · Alejandro Jimenez Rodriguez · Hannes P. Saal

Neuroscience  [Top]

A coupled autoencoder approach for multi-modal analysis of cell types	Rohan Gala · Nathan Gouwens · Zizhen Yao · Agata Budzillo · Osnat Penn · Bosiljka Tasic · Gabe Murphy · Hongkui Zeng · Uygar Sümbül
A neurally plausible model learns successor representations in partially observable environments	Eszter Vértes · Maneesh Sahani
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models	Ruoxi Sun · Ian Kinsella · Scott Linderman · Liam Paninski
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference	Cole Hurwitz · Kai Xu · Akash Srivastava · Alessio Buccino · Matthias Hennig
Weight Agnostic Neural Networks	Adam Gaier · David Ha
A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit	Yanis Bahroun · Dmitri Chklovskii · Anirvan Sengupta
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos	Eleanor Batty · Matthew Whiteway · Shreya Saxena · Dan Biderman · Taiga Abe · Simon Musall · Winthrop Gillis · Jeffrey Markowitz · Anne Churchland · John Cunningham · Sandeep R Datta · Scott Linderman · Liam Paninski
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs	Jonas Kubilius · Martin Schrimpf · Ha Hong · Najib Majaj · Rishi Rajalingham · Elias Issa · Kohitij Kar · Pouya Bashivan · Jonathan Prescott-Roy · Kailyn Schmidt · Aran Nayebi · Daniel Bear · Daniel Yamins · James J DiCarlo
Infra-slow brain dynamics as a marker for cognitive function and decline	Shagun Ajmera Shyam Sunder Ajmera · Shreya Rajagopal · Razi Rehman · Devarajan Sridharan

Perception  [Top]

Metamers of neural networks reveal divergence from human perceptual systems

Jenelle Feather · Alex Durango · Ray Gonzalez · Josh McDermott

Problem Solving  [Top]

Interval timing in deep reinforcement learning agents

Ben Deverett · Ryan Faulkner · Meire Fortunato · Gregory Wayne · Joel Leibo

Reasoning  [Top]

Abstract Reasoning with Distracting Features	Kecheng Zheng · Zheng-Jun Zha · Wei Wei
Learning Perceptual Inference by Contrasting	Chi Zhang · Baoxiong Jia · Feng Gao · Yixin Zhu · HongJing Lu · Song-Chun Zhu

Visual Perception  [Top]

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction	Hidenori Tanaka · Aran Nayebi · Niru Maheswaranathan · Lane McIntosh · Stephen Baccus · Surya Ganguli
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI	Roman Beliy · Guy Gaziv · Assaf Hoogi · Francesca Strappini · Tal Golan · Michal Irani
Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity	Yuan Wang · Michael Tarr · Leila Wehbe
Perceiving the arrow of time in autoregressive motion	Kristof Meding · Dominik Janzing · Bernhard Schölkopf · Felix A. Wichmann
Visual Sequence Learning in Hierarchical Prediction Networks and Primate Visual Cortex	JIELIN QIU · Ge Huang · Tai Sing Lee

Optimization

Combinatorial Optimization  [Top]

A Graph Theoretic Additive Approximation of Optimal Transport	Nathaniel Lahn · Deepika Mulchandani · Sharath Raghvendra
Combinatorial Bayesian Optimization using the Graph Cartesian Product	Changyong Oh · Jakub Tomczak · Efstratios Gavves · Max Welling
Exact Combinatorial Optimization with Graph Convolutional Neural Networks	Maxime Gasse · Didier Chetelat · Nicola Ferroni · Laurent Charlin · Andrea Lodi
Learning Local Search Heuristics for Boolean Satisfiability	Emre Yolcu · Barnabas Poczos
Learning to Perform Local Rewriting for Combinatorial Optimization	Xinyun Chen · Yuandong Tian

Convex Optimization  [Top]

A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers	Hao Yu
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression	JIAJIN LI · SEN HUANG · Anthony Man-Cho So
Acceleration via Symplectic Discretization of High-Resolution Differential Equations	Bin Shi · Simon Du · Weijie Su · Michael Jordan
An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums	Hadrien Hendrikx · Francis Bach · Laurent Massoulié
An adaptive Mirror-Prox method for variational inequalities with singular operators	Kimon Antonakopoulos · Veronica Belmega · Panayotis Mertikopoulos
Blended Matching Pursuit	Cyrille Combettes · Sebastian Pokutta
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients	Jun Sun · Tianyi Chen · Georgios Giannakis · Zaiyue Yang
Complexity of Highly Parallel Non-Smooth Convex Optimization	Sebastien Bubeck · Qijia Jiang · Yin-Tat Lee · Yuanzhi Li · Aaron Sidford
Efficient Symmetric Norm Regression via Linear Sketching	Zhao Song · Ruosong Wang · Lin Yang · Hongyang Zhang · Peilin Zhong
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme	Tao Sun · Yuejiao Sun · Dongsheng Li · Qing Liao
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem	DongDong Ge · Haoyue Wang · Zikai Xiong · Yinyu Ye
Necessary and Sufficient Geometries for Gradient Methods	Daniel Levy · John Duchi
On the Curved Geometry of Accelerated Optimization	Aaron Defazio
Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm	Giulia Luise · Saverio Salzo · Massimiliano Pontil · Carlo Ciliberto
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD	PHUONG_HA NGUYEN · Lam Nguyen · Marten van Dijk
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration	Clarice Poon · Jingwei Liang
A Generic Acceleration Framework for Stochastic Composite Optimization	Andrei Kulunchakov · Julien Mairal
A unified variance-reduced accelerated gradient method for convex optimization	Guanghui Lan · Zhize Li · Yi Zhou
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions	Ashia Wilson · Lester Mackey · Andre Wibisono
Communication trade-offs for Local-SGD with large step size	Aymeric Dieuleveut · Kumar Kshitij Patel
Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control	Miguel Vaquero · Jorge Cortes
Decentralized sketching of low rank matrices	Rakshith Sharma Srinivasa · Kiryung Lee · Marius Junge · Justin Romberg
Differentiable Convex Optimization Layers	Akshay Agrawal · Brandon Amos · Shane Barratt · Stephen Boyd · Steven Diamond · J. Zico Kolter
Dimension-Free Bounds for Low-Precision Training	Zheng Li · Christopher De Sa
Fast and Accurate Stochastic Gradient Estimation	Beidi Chen · Yingchen Xu · Anshumali Shrivastava
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression	Deeksha Adil · Richard Peng · Sushant Sachdeva
Hamiltonian descent for composite objectives	Brendan O'Donoghue · Chris J. Maddison
High-Dimensional Optimization in Adaptive Random Subspaces	Jonathan Lacotte · Mert Pilanci · Marco Pavone
Optimal Stochastic and Online Learning with Individual Iterates	Yunwen Lei · Peng Yang · Ke Tang · Ding-Xuan Zhou
Primal-Dual Block Generalized Frank-Wolfe	Qi Lei · JIACHENG ZHUO · Constantine Caramanis · Inderjit S Dhillon · Alexandros Dimakis
Stochastic Frank-Wolfe for Composite Convex Minimization	Francesco Locatello · Alp Yurtsever · Olivier Fercoq · Volkan Cevher
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization	Adithya M Devraj · Jianshu Chen

Non-Convex Optimization  [Top]

Asymmetric Valleys: Beyond Sharp and Flat Local Minima	Haowei He · Gao Huang · Yang Yuan
Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms	Mahesh Chandra Mukkamala · Peter Ochs
Efficiently escaping saddle points on manifolds	Christopher Criscitiello · Nicolas Boumal
Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities	Wei Qian · Yuqian Zhang · Yudong Chen
Learning dynamic polynomial proofs	Alhussein Fawzi · Mateusz Malinowski · Hamza Fawzi · Omar Fawzi
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems	Yi Xu · Rong Jin · Tianbao Yang
Nonconvex Low-Rank Tensor Completion from Noisy Data	Changxiao Cai · Gen Li · H. Vincent Poor · Yuxin Chen
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates	Sharan Vaswani · Aaron Mishkin · Issam Laradji · Mark Schmidt · Gauthier Gidel · Simon Lacoste-Julien
SpiderBoost and Momentum: Faster Variance Reduction Algorithms	Zhe Wang · Kaiyi Ji · Yi Zhou · Yingbin Liang · Vahid Tarokh
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points	Zhize Li
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk	Shuang Li · Gongguo Tang · Michael B Wakin
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models	Stefano Sarao Mannelli · Giulio Biroli · Chiara Cammarota · Florent Krzakala · Lenka Zdeborová
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning	Zhihui Zhu · Tianyu Ding · Daniel Robinson · Manolis Tsakiris · René Vidal
Competitive Gradient Descent	Florian Schaefer · Anima Anandkumar
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization	Rixon Crane · Fred Roosta
Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent	Huizhuo Yuan · Xiangru Lian · Chris Junchi Li · Ji Liu · Wenqing Hu
Efficiently avoiding saddle points with zero order methods: No gradients required	Emmanouil-Vasileios Vlatakis-Gkaragkounis · Lampros Flokas · Georgios Piliouras
Escaping from saddle points on Riemannian manifolds	Yue Sun · Nicolas Flammarion · Maryam Fazel
Exponentially convergent stochastic k-PCA without variance reduction	Cheng Tang
First-order methods almost always avoid saddle points: The case of vanishing step-sizes	Ioannis Panageas · Georgios Piliouras · Xiao Wang
Learning Sparse Distributions using Iterative Hard Thresholding	Jacky Y Zhang · Rajiv Khanna · Anastasios Kyrillidis · Oluwasanmi Koyejo
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization	Farzin Haddadpour · Mohammad Mahdi Kamani · Mehrdad Mahdavi · Viveck Cadambe
Max-value Entropy Search for Multi-Objective Bayesian Optimization	Syrine Belakaria · Aryan Deshwal · Janardhan Rao Doppa
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods	Maher Nouiehed · Maziar Sanjabi · Tianjian Huang · Jason Lee · Meisam Razaviyayn
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution	Qing Qu · Xiao Li · Zhihui Zhu
An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints	Mehmet Fatih Sahin · Armin eftekhari · Ahmet Alacaoglu · Fabian Latorre · Volkan Cevher
Bayesian Optimization with Unknown Search Space	Huong Ha · Santu Rana · Sunil Gupta · Thanh Nguyen · Hung Tran-The · Svetha Venkatesh
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization	Viet Anh Nguyen · Soroosh Shafieezadeh Abadeh · Man-Chung Yue · Daniel Kuhn · Wolfram Wiesemann
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback	Shuai Zheng · Ziyue Huang · James Kwok
Distributed Low-rank Matrix Factorization With Exact Consensus	Zhihui Zhu · Qiuwei Li · Xinshuo Yang · Gongguo Tang · Michael B Wakin
Efficient Algorithms for Smooth Minimax Optimization	Kiran Thekumparampil · Prateek Jain · Praneeth Netrapalli · Sewoong Oh
Momentum-Based Variance Reduction in Non-Convex SGD	Ashok Cutkosky · Francesco Orabona
Provable Non-linear Inductive Matrix Completion	Kai Zhong · Zhao Song · Prateek Jain · Inderjit S Dhillon
Semi-flat minima and saddle points by embedding neural networks to overparameterization	Kenji Fukumizu · Shoichiro Yamaguchi · Yoh-ichi Mototake · Mirai Tanaka
Shadowing Properties of Optimization Algorithms	Antonio Orvieto · Aurelien Lucchi

Stochastic Optimization  [Top]

Double Quantization for Communication-Efficient Distributed Optimization	Yue Yu · Jiaxiang Wu · Longbo Huang
Optimal Decision Tree with Noisy Outcomes	Su Jia · viswanath nagarajan · Fatemeh Navidi · R Ravi
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up	Dominic Richards · Patrick Rebeschini
RSN: Randomized Subspace Newton	Robert Gower · Dmitry Koralev · Felix Lieder · Peter Richtarik
Towards closing the gap between the theory and practice of SVRG	Othmane Sebbouh · Nidham Gazagnadou · Samy Jelassi · Francis Bach · Robert Gower
UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization	Ali Kavis · Kfir Y. Levy · Francis Bach · Volkan Cevher
A Latent Variational Framework for Stochastic Optimization	Philippe Casgrain
A Stochastic Composite Gradient Method with Incremental Variance Reduction	Junyu Zhang · Lin Xiao
A Universally Optimal Multistage Accelerated Stochastic Gradient Method	Necdet Serhat Aybat · Alireza Fallah · Mert Gurbuzbalaban · Asuman Ozdaglar
On the convergence of single-call stochastic extra-gradient methods	Yu-Guan Hsieh · Franck Iutzeler · Jérôme Malick · Panayotis Mertikopoulos
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning	Aaron Defazio · Leon Bottou
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG	Yujia Jin · Aaron Sidford
Understanding the Role of Momentum in Stochastic Gradient Methods	Igor Gitman · Hunter Lang · Pengchuan Zhang · Lin Xiao
Alleviating Label Switching with Optimal Transport	Pierre Monteiller · Sebastian Claici · Edward Chien · Farzaneh Mirzazadeh · Justin M Solomon · Mikhail Yurochkin
Beating SGD Saturation with Tail-Averaging and Minibatching	Nicole Muecke · Gergely Neu · Lorenzo Rosasco
Continuous-time Models for Stochastic Optimization Algorithms	Antonio Orvieto · Aurelien Lucchi
Distributed estimation of the inverse Hessian by determinantal averaging	Michal Derezinski · Michael W Mahoney
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares	Rong Ge · Sham Kakade · Rahul Kidambi · Praneeth Netrapalli
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging	Pooria Joulani · András György · Csaba Szepesvari
Variance Reduction for Matrix Games	Yair Carmon · Yujia Jin · Aaron Sidford · Kevin Tian

Submodular Optimization  [Top]

Adaptive Sequence Submodularity	Marko Mitrovic · Ehsan Kazemi · Moran Feldman · Andreas Krause · Amin Karbasi
Fast Decomposable Submodular Function Minimization using Constrained Total Variation	Senanayak Sesh Kumar Karri · Francis Bach · Thomas Pock
Fast Parallel Algorithms for Statistical Subset Selection Problems	Sharon Qian · Yaron Singer
Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time	Alan Kuhnle
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback	Mingrui Zhang · Lin Chen · Hamed Hassani · Amin Karbasi
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match	Amin Karbasi · Hamed Hassani · Aryan Mokhtari · Zebang Shen
Submodular Function Minimization with Noisy Evaluation Oracle	Shinji Ito

Probabilistic Methods

Bayesian Nonparametrics  [Top]

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation	Viet Anh Nguyen · Soroosh Shafieezadeh Abadeh · Man-Chung Yue · Daniel Kuhn · Wolfram Wiesemann
Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees	Alix LHERITIER · Frederic Cazals
Random Tessellation Forests	Shufei Ge · Shijia Wang · Yee Whye Teh · Liangliang Wang · Lloyd Elliott

Belief Propagation  [Top]

Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay	Frederic Koehler
Hyper-Graph-Network Decoders for Block Codes	Eliya Nachmani · Lior Wolf

Causal Inference  [Top]

Adapting Neural Networks for the Estimation of Treatment Effects	Claudia Shi · David Blei · Victor Veitch
Causal Regularization	Dominik Janzing
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions	Murat Kocaoglu · Amin Jaber · Karthikeyan Shanmugam · Elias Bareinboim
Debiased Bayesian inference for average treatment effects	Kolyan Ray · Botond Szabo
Deep Generalized Method of Moments for Instrumental Variable Analysis	Andrew Bennett · Nathan Kallus · Tobias Schnabel
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets	Daniel Kumor · Bryant Chen · Elias Bareinboim
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments	Vasilis Syrgkanis · Victor Lei · Miruna Oprescu · Maggie Hei · Keith Battocchi · Greg Lewis
Identification of Conditional Causal Effects under Markov Equivalence	Amin Jaber · Jiji Zhang · Elias Bareinboim
Variance Reduction in Bipartite Experiments through Correlation Clustering	Jean Pouget-Abadie · Kevin Aydin · Warren Schudy · Kay Brodersen · Vahab Mirrokni
Identifying Causal Effects via Context-specific Independence Relations	Santtu Tikka · Antti Hyttinen · Juha Karvanen
Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems	Robert Ness · Kaushal Paneri · Olga Vitek
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes	Junzhe Zhang · Elias Bareinboim
Policy Evaluation with Latent Confounders via Optimal Balance	Andrew Bennett · Nathan Kallus
Sample Efficient Active Learning of Causal Trees	Kristjan Greenewald · Dmitriy Katz · Karthikeyan Shanmugam · Sara Magliacane · Murat Kocaoglu · Enric Boix Adsera · Guy Bresler
Selecting causal brain features with a single conditional independence test per feature	Atalanti Mastakouri · Bernhard Schölkopf · Dominik Janzing
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering	Biwei Huang · Kun Zhang · Pengtao Xie · Mingming Gong · Eric Xing · Clark Glymour
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data	Amanda Gentzel · Dan Garant · David Jensen
Triad Constraints for Learning Causal Structure of Latent Variables	Ruichu Cai · Feng Xie · Clark Glymour · Zhifeng Hao · Kun Zhang
Using Embeddings to Correct for Unobserved Confounding in Networks	Victor Veitch · Yixin Wang · David Blei

Distributed Inference  [Top]

Robust Multi-agent Counterfactual Prediction	Alexander Peysakhovich · Christian Kroer · Adam Lerer
Statistical Model Aggregation via Parameter Matching	Mikhail Yurochkin · Mayank Agarwal · Soumya Ghosh · Kristjan Greenewald · Nghia Hoang

Gaussian Processes  [Top]

Implicit Posterior Variational Inference for Deep Gaussian Processes	Haibin YU · Yizhou Chen · Bryan Kian Hsiang Low · Patrick Jaillet · Zhongxiang Dai
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes	Rui Li
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes	Siqi Liu · Milos Hauskrecht
Offline Contextual Bayesian Optimization	Ian Char · Youngseog Chung · Willie Neiswanger · Kirthevasan Kandasamy · Oak Nelson · Mark Boyer · Egemen Kolemen · Jeff Schneider
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes	Creighton Heaukulani · Mark van der Wilk
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs	Yusuke Tanaka · Toshiyuki Tanaka · Tomoharu Iwata · Takeshi Kurashima · Maya Okawa · Yasunori Akagi · Hiroyuki Toda
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control	Armin Lederer · Jonas Umlauft · Sandra Hirche
Band-Limited Gaussian Processes: The Sinc Kernel	Felipe Tobar
Exact Gaussian Processes on a Million Data Points	Ke Wang · Geoff Pleiss · Jacob Gardner · Stephen Tyree · Kilian Weinberger · Andrew Gordon Wilson
Function-Space Distributions over Kernels	Gregory Benton · Wesley J Maddox · Jayson Salkey · Julio Albinati · Andrew Gordon Wilson
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes	Lingge Li · Dustin Pluta · Babak Shahbaba · Norbert Fortin · Hernando Ombao · Pierre Baldi
Multi-resolution Multi-task Gaussian Processes	Oliver Hamelijnck · Theodoros Damoulas · Kangrui Wang · Mark Girolami
Multi-task Learning for Aggregated Data using Gaussian Processes	Fariba Yousefi · Michael T Smith · Mauricio Álvarez
Structured Variational Inference in Continuous Cox Process Models	Virginia Aglietti · Edwin Bonilla · Theodoros Damoulas · Sally Cripps

Graphical Models  [Top]

An Algorithm to Learn Polytree Networks with Hidden Nodes	Firoozeh Sepehr · Donatello Materassi
Approximating the Permanent by Sampling from Adaptive Partitions	Jonathan Kuck · Tri Dao · Hamid Rezatofighi · Ashish Sabharwal · Stefano Ermon
Bayesian Joint Estimation of Multiple Graphical Models	Lingrui Gan · Xinming Yang · Naveen Narisetty · Feng Liang
Counting the Optimal Solutions in Graphical Models	Radu Marinescu · Rina Dechter
Direct Estimation of Differential Functional Graphical Models	Boxin Zhao · Y. Samuel Wang · Mladen Kolar
On Tractable Computation of Expected Predictions	Pasha Khosravi · YooJung Choi · Yitao Liang · Antonio Vergari · Guy Van den Broeck
Smoothing Structured Decomposable Circuits	Andy Shih · Guy Van den Broeck · Paul Beame · Antoine Amarilli
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models	Shanshan Wu · Sujay Sanghavi · Alexandros Dimakis
Structured Graph Learning Via Laplacian Spectral Constraints	Sandeep Kumar · Jiaxi Ying · Jose Vinicius de Miranda Cardoso · Daniel Palomar

Hierarchical Models  [Top]

Learning Hierarchical Priors in VAEs	Alexej Klushyn · Nutan Chen · Richard Kurle · Botond Cseke · Patrick van der Smagt
Poisson-Randomized Gamma Dynamical Systems	Aaron Schein · Scott Linderman · Mingyuan Zhou · David Blei · Hanna Wallach
Reconciling meta-learning and continual learning with online mixtures of tasks	Ghassen Jerfel · Erin Grant · Tom Griffiths · Katherine Heller

Latent Variable Models  [Top]

Bayesian Learning of Sum-Product Networks	Martin Trapp · Robert Peharz · Hong Ge · Franz Pernkopf · Zoubin Ghahramani
Latent distance estimation for random geometric graphs	Ernesto Araya Valdivia · De Castro Yohann
The continuous Bernoulli: fixing a pervasive error in variational autoencoders	Gabriel Loaiza-Ganem · John Cunningham

MCMC  [Top]

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model	Atilim Gunes Baydin · Lei Shao · Wahid Bhimji · Lukas Heinrich · Saeid Naderiparizi · Andreas Munk · Jialin Liu · Bradley Gram-Hansen · Gilles Louppe · Lawrence Meadows · Philip Torr · Victor Lee · Kyle Cranmer · Mr. Prabhat · Frank Wood
Online sampling from log-concave distributions	Holden Lee · Oren Mangoubi · Nisheeth Vishnoi
Parameter elimination in particle Gibbs sampling	Anna Wigren · Riccardo Sven Risuleo · Lawrence Murray · Fredrik Lindsten
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees	Ruqi Zhang · Christopher De Sa
Pseudo-Extended Markov chain Monte Carlo	Christopher Nemeth · Fredrik Lindsten · Maurizio Filippone · James Hensman
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction	Difan Zou · Pan Xu · Quanquan Gu
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates	Adil SALIM · Dmitry Koralev · Peter Richtarik
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond	Xuechen Li · Yi Wu · Lester Mackey · Murat Erdogdu
The Randomized Midpoint Method for Log-Concave Sampling	Ruoqi Shen · Yin Tat Lee
Computational Separations between Sampling and Optimization	Kunal Talwar
Estimating Convergence of Markov chains with L-Lag Couplings	Niloy Biswas · Pierre E Jacob · Paul Vanetti
Exponential Family Estimation via Adversarial Dynamics Embedding	Bo Dai · Zhen Liu · Hanjun Dai · Niao He · Arthur Gretton · Le Song · Dale Schuurmans
Gradient-based Adaptive Markov Chain Monte Carlo	Michalis Titsias · Petros Dellaportas
On two ways to use determinantal point processes for Monte Carlo integration	Guillaume Gautier · Rémi Bardenet · Michal Valko
Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices	Santosh Vempala · Andre Wibisono
Sample Adaptive MCMC	Michael Zhu
The Implicit Metropolis-Hastings Algorithm	Kirill Neklyudov · Evgenii Egorov · Dmitry Vetrov

Topic Models  [Top]

Discriminative Topic Modeling with Logistic LDA	Iryna Korshunova · Hanchen Xiong · Mateusz Fedoryszak · Lucas Theis
Precision-Recall Balanced Topic Modelling	Seppo Virtanen · Mark Girolami
Scalable inference of topic evolution via models for latent geometric structures	Mikhail Yurochkin · Zhiwei Fan · Aritra Guha · Paraschos Koutris · XuanLong Nguyen

Variational Inference  [Top]

Approximate Inference Turns Deep Networks into Gaussian Processes	Mohammad Emtiyaz Khan · Alexander Immer · Ehsan Abedi · Maciej Korzepa
Copula-like Variational Inference	Marcel Hirt · Petros Dellaportas · Alain Durmus
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation	Justin Domke · Daniel Sheldon
Importance Weighted Hierarchical Variational Inference	Artem Sobolev · Dmitry Vetrov
Practical Deep Learning with Bayesian Principles	Kazuki Osawa · Siddharth Swaroop · Mohammad Emtiyaz Khan · Anirudh Jain · Runa Eschenhagen · Richard E Turner · Rio Yokota
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data	Dominik Linzner · Michael Schmidt · Heinz Koeppl
Universal Boosting Variational Inference	Trevor Campbell · Xinglong Li
Variational Bayes under Model Misspecification	Yixin Wang · David Blei
Variational Bayesian Decision-making for Continuous Utilities	Tomasz Kuśmierczyk · Joseph Sakaya · Arto Klami
Variational Bayesian Optimal Experimental Design	Adam Foster · Martin Jankowiak · Elias Bingham · Paul Horsfall · Yee Whye Teh · Thomas Rainforth · Noah Goodman
A New Distribution on the Simplex with Auto-Encoding Applications	Andrew Stirn · Tony Jebara · David Knowles
Bayesian Layers: A Module for Neural Network Uncertainty	Dustin Tran · Mike Dusenberry · Mark van der Wilk · Danijar Hafner
Streaming Bayesian Inference for Crowdsourced Classification	Edoardo Manino · Long Tran-Thanh · Nicholas Jennings
Learning Hawkes Processes from a handful of events	Farnood Salehi · William Trouleau · Matthias Grossglauser · Patrick Thiran
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions	Peng Chen · Keyi Wu · Joshua Chen · Tom O'Leary-Roseberry · Omar Ghattas
Provable Gradient Variance Guarantees for Black-Box Variational Inference	Justin Domke
Semi-Implicit Graph Variational Auto-Encoders	Arman Hasanzadeh · Ehsan Hajiramezanali · Krishna Narayanan · Nick Duffield · Mingyuan Zhou · Xiaoning Qian
Sparse Variational Inference: Bayesian Coresets from Scratch	Trevor Campbell · Boyan Beronov
Stein Variational Gradient Descent With Matrix-Valued Kernels	Dilin Wang · Ziyang Tang · Chandrajit Bajaj · Qiang Liu
Tensor Monte Carlo: Particle Methods for the GPU era	Laurence Aitchison
The Thermodynamic Variational Objective	Vaden Masrani · Tuan Anh Le · Frank Wood

Reinforcement Learning and Planning

Decision and Control  [Top]

Generalized Off-Policy Actor-Critic	Shangtong Zhang · Wendelin Boehmer · Shimon Whiteson
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints	Sebastian Tschiatschek · Ahana Ghosh · Luis Haug · Rati Devidze · Adish Singla
Logarithmic Regret for Online Control	Naman Agarwal · Elad Hazan · Karan Singh
Adaptive Auxiliary Task Weighting for Reinforcement Learning	Xingyu Lin · Harjatin Baweja · George Kantor · David Held
Causal Confusion in Imitation Learning	Pim de Haan · Dinesh Jayaraman · Sergey Levine
Hierarchical Decision Making by Generating and Following Natural Language Instructions	Hengyuan Hu · Denis Yarats · Qucheng Gong · Yuandong Tian · Mike Lewis
Non-Cooperative Inverse Reinforcement Learning	Xiangyuan Zhang · Kaiqing Zhang · Erik Miehling · Tamer Basar
Robust exploration in linear quadratic reinforcement learning	Jack Umenberger · Mina Ferizbegovic · Thomas Schön · Håkan Hjalmarsson
Compositional Plan Vectors	Coline Devin · Daniel Geng · Pieter Abbeel · Trevor Darrell · Sergey Levine
Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis	Yingying Li · Xin Chen · Na Li
Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games	Kaiqing Zhang · Zhuoran Yang · Tamer Basar
Policy Continuation with Hindsight Inverse Dynamics	Hao Sun · Zhizhong Li · Xiaotong Liu · Bolei Zhou · Dahua Lin

Exploration  [Top]

A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning	Francisco Garcia · Philip Thomas
Limiting Extrapolation in Linear Approximate Value Iteration	Andrea Zanette · Alessandro Lazaric · Mykel J Kochenderfer · Emma Brunskill
Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters	Alberto Maria Metelli · Amarildo Likmeta · Marcello Restelli
Provably Efficient Q-Learning with Low Switching Cost	Yu Bai · Tengyang Xie · Nan Jiang · Yu-Xiang Wang
Regret Bounds for Learning State Representations in Reinforcement Learning	Ronald Ortner · Matteo Pirotta · Alessandro Lazaric · Ronan Fruit · Odalric-Ambrym Maillard
Safe Exploration for Interactive Machine Learning	Matteo Turchetta · Felix Berkenkamp · Andreas Krause
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning	David Janz · Jiri Hron · Przemysław Mazur · Katja Hofmann · José Miguel Hernández-Lobato · Sebastian Tschiatschek
Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model	Andrea Zanette · Mykel J Kochenderfer · Emma Brunskill
Better Exploration with Optimistic Actor Critic	Kamil Ciosek · Quan Vuong · Robert Loftin · Katja Hofmann
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle	Simon Du · Yuping Luo · Ruosong Wang · Hanrui Zhang
Explicit Planning for Efficient Exploration in Reinforcement Learning	Liangpeng Zhang · Ke Tang · Xin Yao
Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs	Jian QIAN · Ronan Fruit · Matteo Pirotta · Alessandro Lazaric
Information-Theoretic Confidence Bounds for Reinforcement Learning	Xiuyuan Lu · Benjamin Van Roy
Worst-Case Regret Bounds for Exploration via Randomized Value Functions	Daniel Russo

Hierarchical RL  [Top]

DAC: The Double Actor-Critic Architecture for Learning Options	Shangtong Zhang · Shimon Whiteson
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards	Siyuan Li · Rui Wang · Minxue Tang · Chongjie Zhang
Language as an Abstraction for Hierarchical Deep Reinforcement Learning	YiDing Jiang · Shixiang (Shane) Gu · Kevin Murphy · Chelsea Finn
Learning Robust Options by Conditional Value at Risk Optimization	Takuya Hiraoka · Takahisa Imagawa · Tatsuya Mori · Takashi Onishi · Yoshimasa Tsuruoka
The Option Keyboard: Combining Skills in Reinforcement Learning	Andre Barreto · Diana Borsa · Shaobo Hou · Gheorghe Comanici · Eser Aygün · Philippe Hamel · Daniel Toyama · Jonathan hunt · Shibl Mourad · David Silver · Doina Precup

Markov Decision Processes  [Top]

A Family of Robust Stochastic Operators for Reinforcement Learning	Yingdong Lu · Mark Squillante · Chai Wah Wu
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment	Felix Leibfried · Sergio Pascual-Díaz · Jordi Grau-Moya
Finite-Sample Analysis for SARSA with Linear Function Approximation	Shaofeng Zou · Tengyu Xu · Yingbin Liang
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards	Falcon Dai · Matthew Walter
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs	Max Simchowitz · Kevin Jamieson
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives	Wang Chi Cheung
Sampling Networks and Aggregate Simulation for Online POMDP Planning	Hao(Jackson) Cui · Roni Khardon
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples	Tengyu Xu · Shaofeng Zou · Yingbin Liang
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm	Amir-massoud Farahmand

Model-Based RL  [Top]

Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs	Marek Petrik · Reazul Hasan Russel
Correlation Priors for Reinforcement Learning	Bastian Alt · Adrian Šošić · Heinz Koeppl
Explicit Explore-Exploit Algorithms in Continuous State Spaces	Mikael Henaff
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction	Daniel Freeman · David Ha · Luke Metz
Mapping State Space using Landmarks for Universal Goal Reaching	Zhiao Huang · Hao Su · Fangchen Liu
Regularizing Trajectory Optimization with Denoising Autoencoders	Rinu Boney · Norman Di Palo · Mathias Berglund · Alexander Ilin · Juho Kannala · Antti Rasmus · Harri Valpola
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies	Yonathan Efroni · Nadav Merlis · Mohammad Ghavamzadeh · Shie Mannor
When to Trust Your Model: Model-Based Policy Optimization	Michael Janner · Justin Fu · Marvin Zhang · Sergey Levine
When to use parametric models in reinforcement learning?	Hado van Hasselt · Matteo Hessel · John Aslanides

Multi-Agent RL  [Top]

A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning	Nicolas Carion · Nicolas Usunier · Gabriel Synnaeve · Alessandro Lazaric
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control	Sai Qian Zhang · Qi Zhang · Jieyu Lin
Learning Mean-Field Games	Xin Guo · Anran Hu · Renyuan Xu · Junzi Zhang
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity	Deepak Pathak · Christopher Lu · Trevor Darrell · Phillip Isola · Alexei Efros
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning	Yali Du · Lei Han · Meng Fang · Ji Liu · Tianhong Dai · Dacheng Tao
On the Utility of Learning about Humans for Human-AI Coordination	Micah Carroll · Rohin Shah · Mark Ho · Tom Griffiths · Sanjit Seshia · Pieter Abbeel · Anca Dragan
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning	Chao Qu · Shie Mannor · Huan Xu · Yuan Qi · Le Song · Junwu Xiong
Biases for Emergent Communication in Multi-agent Reinforcement Learning	Tom Eccles · Yoram Bachrach · Guy Lever · Angeliki Lazaridou · Thore Graepel
Ease-of-Teaching and Language Structure from Emergent Communication	Fushan Li · Michael Bowling
Finding Friend and Foe in Multi-Agent Games	Jack Serrino · Max Kleiman-Weiner · David Parkes · Josh Tenenbaum
Learning Fairness in Multi-Agent Systems	Jiechuan Jiang · Zongqing Lu
MAVEN: Multi-Agent Variational Exploration	Anuj Mahajan · Tabish Rashid · Mikayel Samvelyan · Shimon Whiteson
Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach	Shuyue Hu · Chin-wing Leung · Ho-fung Leung
Multi-Agent Common Knowledge Reinforcement Learning	Christian Schroeder de Witt · Jakob Foerster · Gregory Farquhar · Philip Torr · Wendelin Boehmer · Shimon Whiteson

Navigation  [Top]

Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs	Himanshu Sahni · Toby Buckley · Pieter Abbeel · Ilya Kuzovkin
Chasing Ghosts: Instruction Following as Bayesian State Tracking	Peter Anderson · Ayush Shrivastava · Devi Parikh · Dhruv Batra · Stefan Lee

Planning  [Top]

Control What You Can: Intrinsically Motivated Task-Planning Agent	Sebastian Blaes · Marin Vlastelica Pogančić · Jiajie Zhu · Georg Martius
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning	Akihiro Kishimoto · Beat Buesser · Bei Chen · Adi Botea
Maximum Entropy Monte-Carlo Planning	Chenjun Xiao · Ruitong Huang · Jincheng Mei · Dale Schuurmans · Martin Müller
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning	Erwan Lecarpentier · Emmanuel Rachelson
Planning in entropy-regularized Markov decision processes and games	Jean-Bastien Grill · Omar Darwiche Domingues · Pierre Menard · Remi Munos · Michal Valko
Planning with Goal-Conditioned Policies	Soroush Nasiriany · Vitchyr Pong · Steven Lin · Sergey Levine
Regression Planning Networks	Danfei Xu · Roberto Martín-Martín · De-An Huang · Yuke Zhu · Silvio Savarese · Li Fei-Fei
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning	Ben Eysenbach · Russ Salakhutdinov · Sergey Levine

Reinforcement Learning  [Top]

Convergent Policy Optimization for Safe Reinforcement Learning	Ming Yu · Zhuoran Yang · Mladen Kolar · Zhaoran Wang
Experience Replay for Continual Learning	David Rolnick · Arun Ahuja · Jonathan Schwarz · Timothy Lillicrap · Gregory Wayne
Exploration via Hindsight Goal Generation	Zhizhou Ren · Kefan Dong · Yuan Zhou · Qiang Liu · Jian Peng
Hindsight Credit Assignment	Anna Harutyunyan · Will Dabney · Thomas Mesnard · Mohammad Gheshlaghi Azar · Bilal Piot · Nicolas Heess · Hado van Hasselt · Gregory Wayne · Satinder Singh · Doina Precup · Remi Munos
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement	Chao Yang · Xiaojian Ma · Wenbing Huang · Fuchun Sun · Huaping Liu · Junzhou Huang · Chuang Gan
Importance Resampling for Off-policy Prediction	Matthew Schlegel · Wesley Chung · Daniel Graves · Jian Qian · Martha White
Learning Compositional Neural Programs with Recursive Tree Search and Planning	Thomas PIERROT · Guillaume Ligner · Scott Reed · Olivier Sigaud · Nicolas Perrin · Alexandre Laterre · David Kas · Karim Beguir · Nando de Freitas
Multi-View Reinforcement Learning	Minne Li · Lisheng Wu · Jun WANG · Haitham Bou Ammar
Real-Time Reinforcement Learning	Simon Ramstedt · Chris Pal
Reconciling λ-Returns with Experience Replay	Brett Daley · Christopher Amato
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function	Zihan Zhang · Xiangyang Ji
Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update	Su Young Lee · Choi Sungik · Sae-Young Chung
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling	Andrey Kolobov · Yuval Peres · Cheng Lu · Eric Horvitz
Trust Region-Guided Proximal Policy Optimization	Yuhui Wang · Hao He · Xiaoyang Tan · Yaozhong Gan
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning	Harm Van Seijen · Mehdi Fatemi · Arash Tavakoli
A Geometric Perspective on Optimal Representations for Reinforcement Learning	Marc Bellemare · Will Dabney · Robert Dadashi · Adrien Ali Taiga · Pablo Samuel Castro · Nicolas Le Roux · Dale Schuurmans · Tor Lattimore · Clare Lyle
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning	Wenhao Yang · Xiang Li · Zhihua Zhang
Constrained Reinforcement Learning Has Zero Duality Gap	Santiago Paternain · Luiz Chamon · Miguel Calvo-Fullana · Alejandro Ribeiro
Distributional Reward Decomposition for Reinforcement Learning	Zichuan Lin · Li Zhao · Derek Yang · Tao Qin · Tie-Yan Liu · Guangwen Yang
Divergence-Augmented Policy Optimization	Qing Wang · Yingru Li · Jiechao Xiong · Tong Zhang
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections	Ofir Nachum · Yinlam Chow · Bo Dai · Lihong Li
Fast Efficient Hyperparameter Tuning for Policy Gradient Methods	Supratik Paul · Vitaly Kurin · Shimon Whiteson
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning	Harsh Gupta · R. Srikant · Lei Ying
Fully Parameterized Quantile Function for Distributional Reinforcement Learning	Derek Yang · Li Zhao · Zichuan Lin · Tao Qin · Jiang Bian · Tie-Yan Liu
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning	Nathan Kallus · Masatoshi Uehara
Learning Reward Machines for Partially Observable Reinforcement Learning	Rodrigo Toro Icarte · Ethan Waldie · Toryn Klassen · Rick Valenzano · Margarita Castro · Sheila McIlraith
Off-Policy Evaluation via Off-Policy Classification	Alexander Irpan · Kanishka Rao · Konstantinos Bousmalis · Chris Harris · Julian Ibarz · Sergey Levine
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies	Seyed Kamyar Seyed Ghasemipour · Shixiang (Shane) Gu · Richard Zemel
Variance Reduced Policy Evaluation with Smooth Function Approximation	Hoi-To Wai · Mingyi Hong · Zhuoran Yang · Zhaoran Wang · Kexin Tang
VIREL: A Variational Inference Framework for Reinforcement Learning	Matthew Fellows · Anuj Mahajan · Tim G. J. Rudner · Shimon Whiteson
Budgeted Reinforcement Learning in Continuous State Space	Nicolas Carrara · Edouard Leurent · Romain Laroche · Tanguy Urvoy · Odalric-Ambrym Maillard · Olivier Pietquin
Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory	Bin Hu · Usman Syed
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization	Krzysztof M Choromanski · Aldo Pacchiano · Jack Parker-Holder · Yunhao Tang · Vikas Sindhwani
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards	Alexander Trott · Stephan Zheng · Caiming Xiong · Richard Socher
Learning from Trajectories via Subgoal Discovery	Sujoy Paul · Jeroen Vanbaar · Amit Roy-Chowdhury
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning	Gregory Farquhar · Shimon Whiteson · Jakob Foerster
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling	Tengyang Xie · Yifei Ma · Yu-Xiang Wang
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables	Lantao Yu · Tianhe Yu · Chelsea Finn · Stefano Ermon
Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy	Boyi Liu · Qi Cai · Zhuoran Yang · Zhaoran Wang
Neural Temporal-Difference Learning Converges to Global Optima	Qi Cai · Zhuoran Yang · Jason Lee · Zhaoran Wang
Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost	Zhuoran Yang · Yongxin Chen · Mingyi Hong · Zhaoran Wang
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning	Wenjie Shi · Shiji Song · Hui Wu · Ya-Chu Hsu · Cheng Wu · Gao Huang
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction	Aviral Kumar · Justin Fu · George Tucker · Sergey Levine
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making	Minmin Chen · Ramki Gummadi · Chris Harris · Dale Schuurmans
Discovery of Useful Questions as Auxiliary Tasks	Vivek Veeriah · Matteo Hessel · Zhongwen Xu · Janarthanan Rajendran · Richard L Lewis · Junhyuk Oh · Hado van Hasselt · David Silver · Satinder Singh
A Composable Specification Language for Reinforcement Learning Tasks	Kishor Jothimurugan · Rajeev Alur · Osbert Bastani
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation	Runzhe Yang · Xingyuan Sun · Karthik Narasimhan
A Kernel Loss for Solving the Bellman Equation	Yihao Feng · Lihong Li · Qiang Liu
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates	Carlos Riquelme · Hugo Penedones · Damien Vincent · Hartmut Maennel · Sylvain Gelly · Timothy A Mann · Andre Barreto · Gergely Neu
Curriculum-guided Hindsight Experience Replay	Meng Fang · Tianyi Zhou · Yali Du · Lei Han · Zhengyou Zhang
Distributional Policy Optimization: An Alternative Approach for Continuous Control	Chen Tessler · Guy Tennenholtz · Shie Mannor
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation	Samuel Ainsworth · Matt Barnes · Siddhartha Srinivasa
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck	Maximilian Igl · Kamil Ciosek · Yingzhen Li · Sebastian Tschiatschek · Cheng Zhang · Sam Devlin · Katja Hofmann
Goal-conditioned Imitation Learning	Yiming Ding · Carlos Florensa · Pieter Abbeel · Mariano Phielipp
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning	Mahmoud ("Mido") Assran · Joshua Romoff · Nicolas Ballas · Joelle Pineau · Mike Rabbat
Imitation-Projected Programmatic Reinforcement Learning	Abhinav Verma · Hoang Le · Yisong Yue · Swarat Chaudhuri
Reinforcement Learning with Convex Constraints	Sobhan Miryoosefi · Kianté Brantley · Hal Daume III · Miro Dudik · Robert Schapire
RUDDER: Return Decomposition for Delayed Rewards	Jose A. Arjona-Medina · Michael Gillhofer · Michael Widrich · Thomas Unterthiner · Johannes Brandstetter · Sepp Hochreiter
Shaping Belief States with Generative Environment Models for RL	Karol Gregor · Danilo Jimenez Rezende · Frederic Besse · Yan Wu · Hamza Merzic · Aaron van den Oord
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents	Alexander Mott · Daniel Zoran · Mike Chrzanowski · Daan Wierstra · Danilo Jimenez Rezende

Theory

Computational Complexity  [Top]

(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs	Boaz Barak · Chi-Ning Chou · Zhixian Lei · Tselil Schramm · Yueqi Sheng
The Parameterized Complexity of Cascading Portfolio Scheduling	Eduard Eiben · Robert Ganian · Iyad Kanj · Stefan Szeider

Control Theory  [Top]

Certainty Equivalence is Efficient for Linear Quadratic Control

Horia Mania · Stephen Tu · Benjamin Recht

Frequentist Statistics  [Top]

ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls	Jinjin Tian · Aaditya Ramdas
Conformal Prediction Under Covariate Shift	Ryan Tibshirani · Rina Foygel Barber · Emmanuel Candes · Aaditya Ramdas
Power analysis of knockoff filters for correlated designs	Jingbo Liu · Philippe Rigollet
Concentration of risk measures: A Wasserstein distance approach	Sanjay P. Bhat · Prashanth L.A.
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem	Gonzalo Mena · Jonathan Niles-Weed

Game Theory and Computational Economics  [Top]

A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions	Yuan Deng · Sébastien Lahaie · Vahab Mirrokni
Equitable Stable Matchings in Quadratic Time	Nikolaos Tziavelis · Ioannis Giannakopoulos · Katerina Doka · Nectarios Koziris · Panagiotis Karras
Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes	James Bailey · Georgios Piliouras
Learning Auctions with Robust Incentive Guarantees	Jacob Abernethy · Rachel Cummings · Bhuvesh Kumar · Sam Taggart · Jamie Morgenstern
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer	Arsenii Vanunts · Alexey Drutsa
Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions	Gabriele Farina · Christian Kroer · Tuomas Sandholm
Efficient and Thrifty Voting by Any Means Necessary	Debmalya Mandal · Ariel Procaccia · Nisarg Shah · David Woodruff
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium	Gabriele Farina · Chun Kai Ling · Fei Fang · Tuomas Sandholm
Learning to Correlate in Multi-Player General-Sum Sequential Games	Andrea Celli · Alberto Marchesi · Tommaso Bianchi · Nicola Gatti
Multiagent Evaluation under Incomplete Information	Mark Rowland · Shayegan Omidshafiei · Karl Tuyls · Julien Perolat · Michal Valko · Georgios Piliouras · Remi Munos
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games	Emmanouil-Vasileios Vlatakis-Gkaragkounis · Lampros Flokas · Georgios Piliouras
Strategizing against No-regret Learners	Yuan Deng · Jon Schneider · Balasubramanian Sivan
Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks	Gabriele Farina · Chun Kai Ling · Fei Fang · Tuomas Sandholm
Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design	Faidra Georgia Monachou · Itai Ashlagi
Distinguishing Distributions When Samples Are Strategically Transformed	Hanrui Zhang · Yu Cheng · Vincent Conitzer
Manipulating a Learning Defender and Ways to Counteract	Jiarui Gan · Qingyu Guo · Long Tran-Thanh · Bo An · Michael Wooldridge
Prior-Free Dynamic Auctions with Low Regret Buyers	Yuan Deng · Jon Schneider · Balasubramanian Sivan

Hardness of Learning and Approximations  [Top]

Approximation Ratios of Graph Neural Networks for Combinatorial Problems	Ryoma Sato · Makoto Yamada · Hisashi Kashima
Deep ReLU Networks Have Surprisingly Few Activation Patterns	Boris Hanin · David Rolnick
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds	Minshuo Chen · Haoming Jiang · Wenjing Liao · Tuo Zhao
Efficient Deep Approximation of GMMs	Shirin Jalali · Carl Nuzman · Iraj Saniee
Universal Invariant and Equivariant Graph Neural Networks	Nicolas Keriven · Gabriel Peyré

Information Theory  [Top]

Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models	Aditya Gangrade · Praveen Venkatesh · Bobak Nazer · Venkatesh Saligrama
Estimating Entropy of Distributions in Constant Space	Jayadev Acharya · Sourbh Bhadane · Piotr Indyk · Ziteng Sun
Gradient Information for Representation and Modeling	Jie Ding · Robert Calderbank · Vahid Tarokh
On The Classification-Distortion-Perception Tradeoff	Dong Liu · Haochen Zhang · Zhiwei Xiong
Statistical-Computational Tradeoff in Single Index Models	Lingxiao Wang · Zhuoran Yang · Zhaoran Wang
Structure Learning with Side Information: Sample Complexity	Saurabh Sihag · Ali Tajer
The spiked matrix model with generative priors	Benjamin Aubin · Bruno Loureiro · Antoine Maillard · Florent Krzakala · Lenka Zdeborová
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels	Yihan Jiang · Hyeji Kim · Himanshu Asnani · Sreeram Kannan · Sewoong Oh · Pramod Viswanath

Large Deviations and Asymptotic Analysis  [Top]

McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds	Rui Zhang · Xingwu Liu · Yuyi Wang · Liwei Wang
Nonzero-sum Adversarial Hypothesis Testing Games	Sarath Yasodharan · Patrick Loiseau
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance	Kimia Nadjahi · Alain Durmus · Umut Simsekli · Roland Badeau
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond	Arindam Banerjee · Qilong Gu · Vidyashankar Sivakumar · Steven Wu

Learning Theory  [Top]

Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence	Fengxiang He · Tongliang Liu · Dacheng Tao
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation	Colin Wei · Tengyu Ma
Exact inference in structured prediction	Kevin Bello · Jean Honorio
Globally optimal score-based learning of directed acyclic graphs in high-dimensions	Bryon Aragam · Arash Amini · Qing Zhou
List-decodable Linear Regression	Sushrut Karmalkar · Adam Klivans · Pravesh Kothari
On the Calibration of Multiclass Classification with Rejection	Chenri Ni · Nontawat Charoenphakdee · Junya Honda · Masashi Sugiyama
On the Hardness of Robust Classification	Pascale Gourdeau · Varun Kanade · Marta Kwiatkowska · James Worrell
Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation	Chen Dan · Hong Wang · Hongyang Zhang · Yuchen Zhou · Pradeep Ravikumar
PAC-Bayes under potentially heavy tails	Matthew Holland
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection	Yihe Dong · Samuel Hopkins · Jerry Li
Uniform convergence may be unable to explain generalization in deep learning	Vaishnavh Nagarajan · J. Zico Kolter
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families	Brian Axelrod · Ilias Diakonikolas · Alistair Stewart · Anastasios Sidiropoulos · Gregory Valiant
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks	Gaël Letarte · Pascal Germain · Benjamin Guedj · Francois Laviolette
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator	Karl Krauth · Stephen Tu · Benjamin Recht
Hypothesis Set Stability and Generalization	Dylan Foster · Spencer Greenberg · Satyen Kale · Haipeng Luo · Mehryar Mohri · Karthik Sridharan
Minimizers of the Empirical Risk and Risk Monotonicity	Marco Loog · Tom Viering · Alexander Mey
Multiclass Learning from Contradictions	Sauptik Dhar · Vladimir Cherkassky · Mohak Shah
On the Correctness and Sample Complexity of Inverse Reinforcement Learning	Abi Komanduru · Jean Honorio
On the Power and Limitations of Random Features for Understanding Neural Networks	Gilad Yehudai · Ohad Shamir
Robustness to Adversarial Perturbations in Learning from Incomplete Data	Amir Najafi · Shin-ichi Maeda · Masanori Koyama · Takeru Miyato
Stability of Graph Scattering Transforms	Fernando Gama · Alejandro Ribeiro · Joan Bruna
State Aggregation Learning from Markov Transition Data	Yaqi Duan · Tracy Ke · Mengdi Wang
Toward a Characterization of Loss Functions for Distribution Learning	Nika Haghtalab · Cameron Musco · Bo Waggoner
An Embedding Framework for Consistent Polyhedral Surrogates	Jessica Finocchiaro · Rafael Frongillo · Bo Waggoner
Covariate-Powered Empirical Bayes Estimation	Nikolaos Ignatiadis · Stefan Wager
Learning Bayesian Networks with Low Rank Conditional Probability Tables	Adarsh Barik · Jean Honorio
Learning to Screen	Alon Cohen · Avinatan Hassidim · Haim Kaplan · Yishay Mansour · Shay Moran
Limits of Private Learning with Access to Public Data	Raef Bassily · Shay Moran · Noga Alon
Multiclass Performance Metric Elicitation	Gaurush Hiranandani · Shant Boodaghians · Ruta Mehta · Oluwasanmi Koyejo
On the Value of Target Data in Transfer Learning	Steve Hanneke · Samory Kpotufe
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering	Ilias Diakonikolas · Daniel Kane · Sushrut Karmalkar · Eric Price · Alistair Stewart
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models	Farnam Mansouri · Yuxin Chen · Ara Vartanian · Jerry Zhu · Adish Singla
Rates of Convergence for Large-scale Nearest Neighbor Classification	Xingye Qiao · Jiexin Duan · Guang Cheng
What Can ResNet Learn Efficiently, Going Beyond Kernels?	Zeyuan Allen-Zhu · Yuanzhi Li
An adaptive nearest neighbor rule for classification	Akshay Balsubramani · Sanjoy Dasgupta · yoav Freund · Shay Moran
Distribution-Independent PAC Learning of Halfspaces with Massart Noise	Ilias Diakonikolas · Themis Gouleakis · Christos Tzamos
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes	Jun Yang · Shengyang Sun · Daniel Roy
Generalization Bounds for Neural Networks via Approximate Description Length	Amit Daniely · Elad Granot
Graph-based Discriminators: Sample Complexity and Expressiveness	Roi Livni · Yishay Mansour
Limitations of Lazy Training of Two-layers Neural Network	Song Mei · Theodor Misiakiewicz · Behrooz Ghorbani · Andrea Montanari
On Making Stochastic Classifiers Deterministic	Andrew Cotter · Maya Gupta · Harikrishna Narasimhan
Semi-Parametric Efficient Policy Learning with Continuous Actions	Victor Chernozhukov · Mert Demirer · Greg Lewis · Vasilis Syrgkanis
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity	Chulhee Yun · Suvrit Sra · Ali Jadbabaie
The Broad Optimality of Profile Maximum Likelihood	Yi Hao · Alon Orlitsky
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals	Surbhi Goel · Sushrut Karmalkar · Adam Klivans
A General Framework for Symmetric Property Estimation	Moses Charikar · Kirankumar Shiragur · Aaron Sidford
Generalization Bounds in the Predict-then-Optimize Framework	Othman El Balghiti · Adam Elmachtoub · Paul Grigas · Ambuj Tewari
Generalization Error Analysis of Quantized Compressive Learning	Xiaoyun Li · Ping Li
Implicit Regularization of Accelerated Methods in Hilbert Spaces	Nicolò Pagliana · Lorenzo Rosasco
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates	Jeffrey Negrea · Mahdi Haghifam · Gintare Karolina Dziugaite · Ashish Khisti · Daniel Roy
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin	Ilias Diakonikolas · Daniel Kane · Pasin Manurangsi
PAC-Bayes Un-Expected Bernstein Inequality	Zakaria Mhammedi · Peter Grünwald · Benjamin Guedj
Private Testing of Distributions via Sample Permutations	Maryam Aliakbarpour · Ilias Diakonikolas · Daniel Kane · Ronitt Rubinfeld
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel	Colin Wei · Jason Lee · Qiang Liu · Tengyu Ma
Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection	Xiaoyi Gu · Leman Akoglu · Alessandro Rinaldo
Theoretical Analysis of Adversarial Learning: A Minimax Approach	Zhuozhuo Tu · Jingwei Zhang · Dacheng Tao
Unified Sample-Optimal Property Estimation in Near-Linear Time	Yi Hao · Alon Orlitsky

Regularization  [Top]

Faster width-dependent algorithm for mixed packing and covering LPs	Digvijay Boob · Saurabh Sawlani · Di Wang
First order expansion of convex regularized estimators	Pierre Bellec · Arun Kuchibhotla
On the number of variables to use in principal component regression	Ji Xu · Daniel Hsu
Implicit Regularization for Optimal Sparse Recovery	Tomas Vaskevicius · Varun Kanade · Patrick Rebeschini
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies	Basri Ronen · David Jacobs · Yoni Kasten · Shira Kritchman
How degenerate is the parametrization of neural networks with the ReLU activation function?	Dennis Maximilian Elbrächter · Julius Berner · Philipp Grohs
Implicit Regularization in Deep Matrix Factorization	Sanjeev Arora · Nadav Cohen · Wei Hu · Yuping Luo
The Impact of Regularization on High-dimensional Logistic Regression	Fariborz Salehi · Ehsan Abbasi · Babak Hassibi
The Implicit Bias of AdaGrad on Separable Data	Qian Qian · Xiaoyuan Qian
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence	Aditya Sharad Golatkar · Alessandro Achille · Stefano Soatto

Spaces of Functions and Kernels  [Top]

Gradient Dynamics of Shallow Univariate ReLU Networks	Francis Williams · Matthew Trager · Daniele Panozzo · Claudio Silva · Denis Zorin · Joan Bruna
Kernel quadrature with DPPs	Ayoub Belhadji · Rémi Bardenet · Pierre Chainais
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration	Kwang-Sung Jun · Ashok Cutkosky · Francesco Orabona
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses	Ananya Uppal · Shashank Singh · Barnabas Poczos
On the Expressive Power of Deep Polynomial Neural Networks	Joe Kileel · Matthew Trager · Joan Bruna
On the Inductive Bias of Neural Tangent Kernels	Alberto Bietti · Julien Mairal

Statistical Physics of Learning  [Top]

A Solvable High-Dimensional Model of GAN	Chuang Wang · Hong Hu · Yue Lu
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis	Yuki Yoshida · Masato Okada
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup	Sebastian Goldt · Madhu Advani · Andrew Saxe · Florent Krzakala · Lenka Zdeborová
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise	Thanh Huy Nguyen · Umut Simsekli · Mert Gurbuzbalaban · Gaël RICHARD
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks	Ryo Karakida · Shotaro Akaho · Shun-ichi Amari
Untangling in Invariant Speech Recognition	Cory Stephenson · Jenelle Feather · Suchismita Padhy · Oguz Elibol · Hanlin Tang · Josh McDermott · SueYeon Chung
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes	Greg Yang