Tutorial

Mon 8:00

Bayesian Deep Learning and a Probabilistic Perspective of Model Construction
Andrew Wilson

Affinity Workshop

Mon 11:35

Breakout Session 4.9: Uncertainty Estimation in Bayesian Deep Learning

Poster

Tue 14:00

Thompson Sampling via Local Uncertainty
Zhendong Wang · Mingyuan Zhou

Poster

Tue 8:00

NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Randy Ardywibowo · Shahin Boluki · Xinyu Gong · Zhangyang “Atlas” Wang · Xiaoning Qian

Poster

Thu 6:00

Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj · Yoram Bresler · Bo Li

Poster

Tue 9:00

Source Separation with Deep Generative Priors
Vivek Jayaram · John Thickstun

Poster

Tue 13:00

Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Joost van Amersfoort · Lewis Smith · Yee-Whye Teh · Yarin Gal

Poster

Wed 10:00

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos · Panagiotis Tigas · Rowan McAllister · Nicholas Rhinehart · Sergey Levine · Yarin Gal

Poster

Tue 9:00

Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Daniel Brown · Russell Coleman · Ravi Srinivasan · Scott Niekum

Poster

Wed 13:00

Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Toth · Harald Oberhauser

Poster

Thu 14:00

Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng · Roman Bachmann · Mohammad Emtiyaz Khan

Poster

Tue 7:00

Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
Yusuke Tsuzuku · Issei Sato · Masashi Sugiyama