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stat.ML2026
Optimal Value Inference for Reinforcement Learning
Nan Lu, Ethan Lee, James M. Robins +2
We study offline inference for the optimal value in reinforcement learning under finite state and action spaces. Two new nuisances are derived as fixed points of a self-induced Bel…
stat.ML2018★ 25 cited
De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers
Victor Chernozhukov, Whitney Newey, Rahul Singh
We provide adaptive inference methods, based on regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expecta…