mlsanityData to 14 Oct 2026

Sharper Perturbed-Kullback-Leibler Exponential Tail Bounds for Beta and Dirichlet Distributions

arXiv 2508.07991 ↗First posted 11 Aug 2025math.PR cs.LGcited by 0

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Abstract (arXiv)

This paper presents an improved exponential tail bound for Beta distributions, refining a result in [15]. This improvement is achieved by interpreting their bound as a regular Kullback-Leibler (KL) divergence one, while introducing a specific perturbation η that shifts the mean of the Beta distribution closer to zero within the KL bound. Our contribution is to show that a larger perturbation can be chosen, thereby tightening the bound. We then extend this result from the Beta distribution to Dirichlet distributions and Dirichlet processes (DPs).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 26 references that are in the map. First posted on arXiv 11 Aug 2025.

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References · 5

  1. 2022From Dirichlet to Rubin: Optimistic Exploration in RL without Bonusescited by 22
  2. 2018KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpointscited by 20
  3. 2018On the Non-asymptotic and Sharp Lower Tail Bounds of Random Variablescited by 19
  4. 2016Bayesian Adaptive Data Analysis Guarantees from Subgaussianitycited by 3
  5. 2023Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithmscited by 2

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