Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem
Statistical inference with bandit data presents fundamental challenges owing to adaptive sampling, which violates the independence assumptions underlying classical asymptotic theory. Recent work has identified stability~\citep{laiwei82} as a sufficient condition for valid inference under adaptivity. This paper first provides a refined stability condition, stated in terms of the iterates of an online algorithm, and shows that a large class of regularized stochastic-mirror-descent-style algorithms satisfy it. This refined condition allows us to strengthen the asymptotic results of~\citet{laiwei82} in several ways. First, we derive a non-asymptotic Berry--Esseen bound for the empirical rewar...
| Authors | Budhaditya Halder, Ishan Sengupta, Koustav Chowdhury et al. |
|---|---|
| Primary category | stat.ML |
| Published | 10 March 2026 |
| Last updated at source | 18 June 2026 |
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