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

Paper recorded by Signals 4 on 2026-10-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-02 on arXiv · recorded by Signals 4 on 2026-10-05

Category: cs.LG · 机器学习 · first seen 2026-10-05

Abstract

Stationarity rewards memory, but after a change the same history can mislead. We ask when forgetting should be permitted. E-process-authorized Thompson sampling (e-ATS) gives each arm full-history and discounted Beta states. An anytime-valid e-process first authorizes the discounted state, then a reversible relevance score controls its influence. Before authorization, e-ATS exactly follows optimis

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#392 most recent of 762 cs.LG papers we have recorded · ↑ newer: Amortized Structured Stochastic Variational Inference for Gaussian Pro · ↓ older: On the Convergence of Success Conditioning for Policy Optimization
Cite this page: When May a Bandit Leave Its Anchor? E-Process-Authorized Thompson Sampling under Non-stationarity: the #392 most recent of 762 cs.LG papers we have recorded (as of 2026-10-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/when-may-a-bandit-leave-its-anchor-e-process-authorized-thompson-sampling-under-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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