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  • title: Universal Online Learning: an Optimistically Universal Learning Rule
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            Universal Online Learning: an Optimistically Universal Learning Rule
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            Universal Online Learning: an Optimistically Universal Learning Rule

            Jul 2, 2022

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            Moise Blanchard

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            We study the subject of universal online learning with non-i.i.d. processes for bounded losses. The notion of universally consistent learning was defined by Hanneke in an effort to study learning theory under minimal assumptions, where the objective is to obtain low long-run average loss for any target function. We are interested in characterizing processes for which learning is possible and whether there exist learning rules guaranteed to be universally consistent given the only assumption that…

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            COLT

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            About COLT

            The conference is held annually since 1988 and has become the leading conference on Learning theory by maintaining a highly selective process for submissions. It is committed in high-quality articles in all theoretical aspects of machine learning and related topics.

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