13 OATML Conference papers at NeurIPS 2021

Jannik Kossen, Neil Band, Aidan Gomez, Clare Lyle, Tim G. J. Rudner, Yarin Gal, Binxin (Robin) Ru, Clare Lyle, Lisa Schut, Atılım Güneş Baydin, Tim G. J. Rudner, Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Pascal Notin, Angelos Filos, 11 Oct 2021

OATML group members and collaborators are proud to present 13 papers at NeurIPS 2021 main conference.

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
Jannik Kossen*, Neil Band*, Aidan Gomez, Clare Lyle, Tom Rainforth, Yarin Gal

Speedy Performance Estimation for Neural Architecture Search
Binxin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal
Oral Presentation

Domain Invariant Representation Learning with Domain Density Transformations
A. Tuan Nguyen, Toan Tran, Yarin Gal, Atilim Gunes Baydin

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Andrew Jesson*, Panagiotis Tigas*, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal

Outcome-Driven Reinforcement Learning via Variational Inference
Tim G. J. Rudner*, Vitchyr H. Pong*, Rowan Thomas McAllister, Yarin Gal, Sergey Levine

On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
Tim G. J. Rudner*, Cong Lu*, Michael Osborne, Yarin Gal, Yee Whye Teh

Improving black-box optimization in VAE latent space using decoder uncertainty
Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal

Self-Consistent Models and Values
Gregory Farquhar*, Kate Baumli*, Zita Marinho*, Angelos Filos, Matteo Hessel, Hado van Hasselt, David Silver

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth

Group Equivariant Subsampling
Jin Xu, Hyunjik Kim, Tom Rainforth, Yee Whye Teh

Online Variational Filtering and Parameter Learning
Andrew Campbell, Yuyang Shi, Tom Rainforth, Arnaud Doucet
Oral Presentation

Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
Andrey Malinin, Neil Band, Yarin Gal, Mark Gales, Alexander Ganshin, German Chesnokov, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Denis Roginskiy, Mariya Shmatova, Panagiotis Tigas, Boris Yangel

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks
Neil Band*, Tim G. J. Rudner*, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W Dusenberry, Ghassen Jerfel, Dustin Tran, Yarin Gal

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