2 citations · 3 across the 3 of their papers we have counts for
7 papers
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning
Koulik Khamaru, Eric Xia, Martin J. Wainwright +1
Various algorithms for reinforcement learning (RL) exhibit dramatic variation in their convergence rates as a function of problem structure. Such problem-dependent behavior is not…
Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning
Koulik Khamaru, Eric Xia, Martin J. Wainwright +1
Various algorithms in reinforcement learning exhibit dramatic variability in their convergence rates and ultimate accuracy as a function of the problem structure. Such instance-spe…
Is Temporal Difference Learning Optimal? An Instance-Dependent Analysis
Koulik Khamaru, Ashwin Pananjady, Feng Ruan +2
We address the problem of policy evaluation in discounted Markov decision processes, and provide instance-dependent guarantees on the -error under a generative model.…
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
Dhruv Malik, Ashwin Pananjady, Kush Bhatia +3
We study derivative-free methods for policy optimization over the class of linear policies. We focus on characterizing the convergence rate of these methods when applied to linear-…
Singularity, Misspecification, and the Convergence Rate of EM
Raaz Dwivedi, Nhat Ho, Koulik Khamaru +3
A line of recent work has analyzed the behavior of the Expectation-Maximization (EM) algorithm in the well-specified setting, in which the population likelihood is locally strongly…
Convergence guarantees for a class of non-convex and non-smooth optimization problems
Koulik Khamaru, Martin J. Wainwright
We consider the problem of finding critical points of functions that are non-convex and non-smooth. Studying a fairly broad class of such problems, we analyze the behavior of three…