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.
On the Markov chain central limit theorem
2 Pith papers cite this work, alongside 241 external citations. Polarity classification is still indexing.
2
Pith papers citing it
241
external citations · Crossref
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Weak convergence rates of Markov transition kernels imply variance convergence bounds for Lipschitz functions and chi-squared divergence bounds under reversibility with Lipschitz initial densities.
citing papers explorer
-
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.
-
Implications of weak convergence rates of Markov transition kernels
Weak convergence rates of Markov transition kernels imply variance convergence bounds for Lipschitz functions and chi-squared divergence bounds under reversibility with Lipschitz initial densities.