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Tutorial
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Mon 8:00
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Bayesian Deep Learning and a Probabilistic Perspective of Model Construction
Andrew Wilson
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Affinity Workshop
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Mon 11:35
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Breakout Session 4.9: Uncertainty Estimation in Bayesian Deep Learning
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Poster
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Tue 14:00
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Thompson Sampling via Local Uncertainty
Zhendong Wang · Mingyuan Zhou
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Poster
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Tue 8:00
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NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Randy Ardywibowo · Shahin Boluki · Xinyu Gong · Zhangyang “Atlas” Wang · Xiaoning Qian
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Poster
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Thu 6:00
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Improving Robustness of Deep-Learning-Based Image Reconstruction
Ankit Raj · Yoram Bresler · Bo Li
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Poster
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Tue 9:00
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Source Separation with Deep Generative Priors
Vivek Jayaram · John Thickstun
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Poster
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Tue 13:00
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Uncertainty Estimation Using a Single Deep Deterministic Neural Network
Joost van Amersfoort · Lewis Smith · Yee-Whye Teh · Yarin Gal
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Poster
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Wed 10:00
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Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Angelos Filos · Panagiotis Tigas · Rowan McAllister · Nicholas Rhinehart · Sergey Levine · Yarin Gal
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Poster
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Tue 9:00
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Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences
Daniel Brown · Russell Coleman · Ravi Srinivasan · Scott Niekum
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Poster
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Wed 13:00
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Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Toth · Harald Oberhauser
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Poster
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Thu 14:00
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Training Binary Neural Networks using the Bayesian Learning Rule
Xiangming Meng · Roman Bachmann · Mohammad Emtiyaz Khan
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Poster
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Tue 7:00
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Normalized Flat Minima: Exploring Scale Invariant Definition of Flat Minima for Neural Networks Using PAC-Bayesian Analysis
Yusuke Tsuzuku · Issei Sato · Masashi Sugiyama
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