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Found 4 papers, 0 papers with code
Krylov-Bellman boosting: Super-linear policy evaluation in general state spaces
Second, by combining this meta-result with sample-size dependent guarantees for residual fitting and LSTD computation, we obtain concrete statistical guarantees that depend on the sample size along with the complexity of the function class used to fit the residuals.
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning
As a consequence, we propose a data-dependent stopping rule for instance-optimal algorithms.
Instance-optimality in optimal value estimation: Adaptivity via variance-reduced Q-learning
Various algorithms in reinforcement learning exhibit dramatic variability in their convergence rates and ultimate accuracy as a function of the problem structure.
Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models
In this work, we provide a rigorous study for the posterior distribution of the number of clusters in DPMM under different prior distributions on the parameters and constraints on the distributions of the data.
