The paper establishes the first distribution-dependent sample complexity bounds showing that informative priors reduce required evaluations in multi-fidelity HPO while uninformative priors recover baseline performance.
arXiv preprint, eprint:2211.08572 , series=
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A new posterior sampling algorithm for (ε, δ)-PAC policy identification in tabular MDPs achieves asymptotic optimality in sample complexity and posterior contraction rate with O(S²AH) runtime per episode.
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Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
The paper establishes the first distribution-dependent sample complexity bounds showing that informative priors reduce required evaluations in multi-fidelity HPO while uninformative priors recover baseline performance.
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Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes
A new posterior sampling algorithm for (ε, δ)-PAC policy identification in tabular MDPs achieves asymptotic optimality in sample complexity and posterior contraction rate with O(S²AH) runtime per episode.