73 citations · 324 across the 34 of their papers we have counts for
41 papers
An Exact Distribution-Free Test for Means of Nonnegative Random Variables
Nikos Vlassis, Philip S. Thomas
Let be independent nonnegative random variables, not necessarily identically distributed. Let be ind…
Coagent Networks: Generalized and Scaled
James E. Kostas, Scott M. Jordan, Yash Chandak +5
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic…
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments
Vincent Liu, Yash Chandak, Philip Thomas +1
In this work, we consider the off-policy policy evaluation problem for contextual bandits and finite horizon reinforcement learning in the nonstationary setting. Reusing old data i…
Optimization using Parallel Gradient Evaluations on Multiple Parameters
Yash Chandak, Shiv Shankar, Venkata Gandikota +2
We propose a first-order method for convex optimization, where instead of being restricted to the gradient from a single parameter, gradients from multiple parameters can be used d…
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Yash Chandak, Shiv Shankar, Nathaniel D. Bastian +3
Methods for sequential decision-making are often built upon a foundational assumption that the underlying decision process is stationary. This limits the application of such method…
Low Variance Off-policy Evaluation with State-based Importance Sampling
David M. Bossens, Philip S. Thomas
In many domains, the exploration process of reinforcement learning will be too costly as it requires trying out suboptimal policies, resulting in a need for off-policy evaluation,…