A T-estimation-based procedure for adaptive density estimation and optimal control in offline contextual MDPs without stationarity, providing oracle risk bounds under two loss functions and finite-sample cost guarantees.
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stat.ML 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Decomposes excess risk in nonstationary weighted ERM into learning and drift terms, then proves oracle inequalities under mixing that recover minimax rates in stationary cases.
citing papers explorer
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Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity
A T-estimation-based procedure for adaptive density estimation and optimal control in offline contextual MDPs without stationarity, providing oracle risk bounds under two loss functions and finite-sample cost guarantees.
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Fast Rates for Nonstationary Weighted Risk Minimization
Decomposes excess risk in nonstationary weighted ERM into learning and drift terms, then proves oracle inequalities under mixing that recover minimax rates in stationary cases.