| 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 |