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 |