3 papers
cs.LG2026
A Unifying View of Coverage in Linear Off-Policy Evaluation
Philip Amortila, Audrey Huang, Akshay Krishnamurthy +1
Off-policy evaluation (OPE) is a fundamental task in reinforcement learning (RL). In the classic setting of linear OPE, finite-sample guarantees often take the form $$ \textrm{Eval…
stat.ML2024
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning
Philip Amortila, Tongyi Cao, Akshay Krishnamurthy
A pervasive phenomenon in machine learning applications is distribution shift, where training and deployment conditions for a machine learning model differ. As distribution shift t…
cs.LG2022
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation
Philip Amortila, Nan Jiang, Dhruv Madeka +1
The current paper studies sample-efficient Reinforcement Learning (RL) in settings where only the optimal value function is assumed to be linearly-realizable. It has recently been…