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arXiv Machine Learning By Kun Wang

Optimal No-Regret Learning for Repeated Prophet Inequality

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The paper presents an efficient algorithm for repeated prophet inequalities with prefix feedback, achieving “~O(√T) expected regret”. It uses empirical backward induction, box‑specific reach bonuses, and a relative‑drop aggregation rule to eliminate polynomial dependence on the number of boxes. This resolves an open question from Liu et al. (2025).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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