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
3 Pith papers cite this work, alongside 241 external citations. Polarity classification is still indexing.
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New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.
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.
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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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Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.
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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.