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News/Optimizing Regret
arxiv
PublishedJuly 31, 2026 at 4:00 AM
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Optimizing Regret

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arXiv:2607.18866v2 Announce Type: replace-cross Abstract: Building on the identity that expected regret equals the covariance between costs and decisions, this paper develops a derivative theory of the covariance regret functional. We derive the G\^ateaux derivative, showing that the universal steep

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arxivMAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation2harxivCounterfactual Model Testing Under Distribution Shift2harxivCentralized Critics for N-Agent Ad Hoc Teamwork2harxivWhere and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning2h
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