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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
Tue 14:00 Thompson Sampling via Local Uncertainty
Zhendong Wang · Mingyuan Zhou
Tue 8:00 NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Randy Ardywibowo · Shahin Boluki · Xinyu Gong · Zhangyang “Atlas” Wang · Xiaoning Qian
Thu 6:00 Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj · Yoram Bresler · Bo Li
Tue 9:00 Source Separation with Deep Generative Priors
Vivek Jayaram · John Thickstun
Tue 13:00 Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Joost van Amersfoort · Lewis Smith · Yee-Whye Teh · Yarin Gal
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
Tue 9:00 Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Daniel Brown · Russell Coleman · Ravi Srinivasan · Scott Niekum
Wed 13:00 Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Toth · Harald Oberhauser
Thu 14:00 Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng · Roman Bachmann · Mohammad Emtiyaz Khan
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