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Predict to Minimize Swap Regret for All Payoff-Bounded Tasks
April 23, 2024, 4:41 a.m. | Lunjia Hu, Yifan Wu
cs.LG updates on arXiv.org arxiv.org
Abstract: A sequence of predictions is calibrated if and only if it induces no swap regret to all down-stream decision tasks. We study the Maximum Swap Regret (MSR) of predictions for binary events: the swap regret maximized over all downstream tasks with bounded payoffs. Previously, the best online prediction algorithm for minimizing MSR is obtained by minimizing the K1 calibration error, which upper bounds MSR up to a constant factor. However, recent work (Qiao and Valiant, …
abstract arxiv binary cs.ds cs.lg decision events maximum predictions stat.ml study tasks type
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