677 citations · 2.2k across the 25 of their papers we have counts for
27 papers
Ignorance is Bliss: Robust Control via Information Gating
Manan Tomar, Riashat Islam, Matthew E. Taylor +2
Informational parsimony provides a useful inductive bias for learning representations that achieve better generalization by being robust to noise and spurious correlations. We prop…
Decomposed Mutual Information Estimation for Contrastive Representation Learning
Alessandro Sordoni, Nouha Dziri, Hannes Schulz +3
Recent contrastive representation learning methods rely on estimating mutual information (MI) between multiple views of an underlying context. E.g., we can derive multiple views of…
Pretraining Representations for Data-Efficient Reinforcement Learning
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch +5
Data efficiency is a key challenge for deep reinforcement learning. We address this problem by using unlabeled data to pretrain an encoder which is then finetuned on a small amount…
Representation Learning with Video Deep InfoMax
R Devon Hjelm, Philip Bachman
Self-supervised learning has made unsupervised pretraining relevant again for difficult computer vision tasks. The most effective self-supervised methods involve prediction tasks b…
Data-Efficient Reinforcement Learning with Self-Predictive Representations
Max Schwarzer, Ankesh Anand, Rishab Goel +3
While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interaction with the environment, learning from l…
Deep Reinforcement and InfoMax Learning
Bogdan Mazoure, Remi Tachet des Combes, Thang Doan +2
We begin with the hypothesis that a model-free agent whose representations are predictive of properties of future states (beyond expected rewards) will be more capable of solving a…