Feb. 15, 2024, 5:42 a.m. | Aaron Roth, Mirah Shi

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.08753v1 Announce Type: cross
Abstract: We study the problem of making predictions so that downstream agents who best respond to them will be guaranteed diminishing swap regret, no matter what their utility functions are. It has been known since Foster and Vohra (1997) that agents who best-respond to calibrated forecasts have no swap regret. Unfortunately, the best known algorithms for guaranteeing calibrated forecasts in sequential adversarial environments do so at rates that degrade exponentially with the dimension of the prediction …

abstract agents arxiv cs.gt cs.lg forecasting functions making matter predictions study them type utility will

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