20 citations · 46 across the 4 of their papers we have counts for
8 papers
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.…
Instance-dependent -bounds for policy evaluation in tabular reinforcement learning
Ashwin Pananjady, Martin J. Wainwright
Markov reward processes (MRPs) are used to model stochastic phenomena arising in operations research, control engineering, robotics, and artificial intelligence, as well as communi…
Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation
Avishek Ghosh, Ashwin Pananjady, Adityanand Guntuboyina +1
Max-affine regression refers to a model where the unknown regression function is modeled as a maximum of unknown affine functions for a fixed . This generalizes linea…
A Family of Bayesian Cramér-Rao Bounds, and Consequences for Log-Concave Priors
Efe Aras, Kuan-Yun Lee, Ashwin Pananjady +1
Under minimal regularity assumptions, we establish a family of information-theoretic Bayesian Cramér-Rao bounds, indexed by probability measures that satisfy a logarithmic Sobolev…
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-…
Worst-case vs Average-case Design for Estimation from Fixed Pairwise Comparisons
Ashwin Pananjady, Cheng Mao, Vidya Muthukumar +2
Pairwise comparison data arises in many domains, including tournament rankings, web search, and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items,…