SOURCE-LINKED INTELLIGENCE

Optimal No-Regret Learning for Repeated Prophet Inequality

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

We study repeated prophet inequalities under prefix feedback. In each of $T$ rounds, a learner encounters fresh values drawn independently from $n$ boxes with unknown $[0,1]$-supported distributions in a fixed order and must irrevocably accept one, observing only the prefix up to its stopping box. Regret is measured against the optimal stopping policy that knows the distributions. We give an efficient algorithm achieving $\widetilde O(\sqrt{T})$ expected regret, matching the lower bound up to logarithmic factors. Our algorithm explores directly through near-optimal policies, combining empirica

Read original source ↗ Open in workspace

recordType

paper

region

Global