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arXiv Machine Learning By Mayand Gulati, Kerong Wang, WeiChen Au

When May a Bandit Leave Its Anchor? E-Process-Authorized Thompson Sampling under Non-stationarity

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The paper introduces E‑Process‑Authorized Thompson Sampling (e‑ATS), a bandit algorithm that uses full‑history and discounted Beta states to decide when to forget past rewards in non‑stationary environments. An anytime‑valid e‑process first authorizes the discounted state, after which a reversible relevance score determines its influence; before authorization, e‑ATS behaves exactly like optimistic Thompson sampling (OTS). Experiments show that removing the authorization step increases mean normalized dynamic pseudo‑regret by 38.4 % on a registered suite but decreases it by 7.5 % on a literature‑derived replay suite, indicating that evidence controls when adaptation begins rather than guaranteeing improvement.

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