2nd Symposium on
Advances in Approximate Bayesian Inference

December 8, 2019
Pan Pacific Hotel
300 - 999 Canada Pl
Vancouver, BC V6C 3B5, Canada
Crystal Pavilion


Registration

7:30 - 8:20 Registration + Coffee

Session 1

8:20 - 8:30 Introduction
8:30 - 9:00 Invited Michalis Titsias: Gradient-based Adaptive Markov Chain Monte Carlo
Slides
9:00 - 9:15 Contributed Jakub Swiatkowski: The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Slides
9:15 - 9:45 Invited Michael Gutmann: Robust Optimisation Monte Carlo
Slides
9:45 - 10:05 Spotlights Marko Jarvenpaa, William Wilkinson, Alexander Alemi, Joseph Marino, Jimmy Ba, Anthony Tompkins
Slides
10:05 - 11:00 Coffee Break and Poster Session (Paper IDs 1-32)

Session 2

11:00 - 11:30 Invited Sergey Levine: Reinforcement Learning, Optimal Control, and Probabilistic Inference
11:30 - 11:45 Contributed Iuliia Molchanova: Structured Semi-Implicit Variational Inference
Slides
11:45 - 12:15 Invited Rianne van den Berg: Normalizing Flows for Discrete Data
12:15 - 13:45 Lunch Break (on your own)

Session 3

13:45 - 14:00 Contributed Matthew Hoffman: Langevin Dynamics as Nonparametric Variational Inference
Slides
14:00 - 14:30 Invited Qiang Liu: Steepest Descent Neural Architecture Optimization: Going Beyond Black-Box
14:30 - 14:50 Spotlights Jiaxin Shi, Anna Kuzina, Mark van der Wilk, Xuechen Li, Yaniv Yacoby, Ravid Shwartz-Ziv
Slides
14:50 - 15:45 Coffee Break and Poster Session (Paper IDs 33-68)

Session 4

15:45 - 16:15 Invited Emily Fox: Stochastic Gradient MCMC for Sequential Data Sources
16:15 - 16:30 Contributed Roman Novak: Neural Tangents: Fast and Easy Infinite Neural Networks in Python
Slides
16:30 - 17:00 Invited Christian Robert: ABC Gibbs for Hierarchical Models
17:00 - 18:00 Panel
James Hensman, Matthew Hoffman, Radford Neal, Christian Robert, Sinead Williamson
Moderator: Frank Wood
Video