arXiv Machine Learning
Continuity-Free Near-Minimax Leading-Order Regret for CVaR-UCBVI
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Q-Learning with Fine-Grained Gap-Dependent Regret
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Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization
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arXiv Machine Learning
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Learning in Markovian bandits with non-observable states and constrained decision epochs
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Hugging Face Trending Papers
Jun 8
Asymptotic Optimality of Thompson Sampling for Risk-Averse Bandits with Sub-Gaussian Rewards
We prove that $ρ\text{-}\mathrm{NPTS}_{\mathrm{SG}}$, an anchor-free nonparametric Thompson Sampling algorithm for risk-averse bandits, achieves regret matching the instance-dependent lower bound to leading order in $\log n$, establishing it as asymptotically optimal for any continuous risk functional $ρ$ (CVaR, mean-variance, Sharpe ratio, distortion risk measures, and more) on the class of distributions with bounded density and sub-Gaussian tails, including Gaussian arms. Both this result and its bounded-support counterpart require only continuity of $ρ$: strictly weaker than the dominance condition of prior parametric Thompson Sampling results, and strictly weaker than the Lipschitz condition of UCB-type algorithms, yielding the first instance-optimal guarantees for non-Lipschitz functionals such as the Sharpe ratio without parametric reward assumptions.
arXiv Machine Learning
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Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret
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