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  • title: Online PAC-Bayes Learning
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            Online PAC-Bayes Learning
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            Online PAC-Bayes Learning

            Nov 28, 2022

            Speakers

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            Maxime Haddouche

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            BG

            Benjamin Guedj

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            About

            Most PAC-Bayesian bounds hold in the batch learning setting where data is collected at once, prior to inference or prediction. This somewhat departs from many contemporary learning problems where data streams are collected and the algorithms must dynamically adjust. We prove new PAC-Bayesian bounds in this online learning framework, leveraging an updated definition of regret, and we revisit classical PAC-Bayesian results with a batch-to-online conversion, extending their remit to the case of dep…

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            NeurIPS 2022

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