369 citations · 447 across the 15 of their papers we have counts for
7 papers · 1 filter
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Michael Dennis, Natasha Jaques, Eugene Vinitsky +4
A wide range of reinforcement learning (RL) problems - including robustness, transfer learning, unsupervised RL, and emergent complexity - require specifying a distribution of task…
Robust Reinforcement Learning using Adversarial Populations
Eugene Vinitsky, Yuqing Du, Kanaad Parvate +3
Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are…
ResiliNet: Failure-Resilient Inference in Distributed Neural Networks
Ashkan Yousefpour, Brian Q. Nguyen, Siddartha Devic +5
Federated Learning aims to train distributed deep models without sharing the raw data with the centralized server. Similarly, in distributed inference of neural networks, by partit…
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
Cathy Wu, Aravind Rajeswaran, Yan Duan +5
Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exa…
Minimizing Regret on Reflexive Banach Spaces and Learning Nash Equilibria in Continuous Zero-Sum Games
Maximilian Balandat, Walid Krichene, Claire Tomlin +1
We study a general version of the adversarial online learning problem. We are given a decision set in a reflexive Banach space and a sequence of reward vectors in…
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies
Francois W. Belletti, Evan R. Sparks, Michael J. Franklin +2
Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering. Much of the existing literature in linear causa…