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  • title: Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
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            Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
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            Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm

            Nov 28, 2022

            Speakers

            CW

            Changlong Wu

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            MH

            Mohsen Heidari

            Speaker · 0 followers

            AG

            Ananth Grama

            Speaker · 0 followers

            About

            We study sequential general online regression, known also as sequential probability assignments, under logarithmic loss when compared against a broad class of experts. We obtain tight, often matching, lower and upper bounds for sequential minimax regret, which is defined as the excess loss incurred by the predictor over the best expert in the class. After proving a general upper bound we consider some specific classes of experts from Lipschitz class to bounded Hessian class and derive matching l…

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            NeurIPS 2022

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