Online Learning with an Almost Perfect Expert

Simina Brânzei; Yuval Peres

doi:10.1073/pnas.1818908116

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arXiv · 1807.11169

Online Learning with an Almost Perfect Expert

Simina Brânzei·Yuval Peres

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Abstract

We study the multiclass online learning problem where a forecaster makes a sequence of predictions using the advice of $n$ experts. Our main contribution is to analyze the regime where the best expert makes at most $b$ mistakes and to show that when $b = o(\log_4{n})$, the expected number of mistakes made by the optimal forecaster is at most $\log_4{n} + o(\log_4{n})$. We also describe an adversary strategy showing that this bound is tight and that the worst case is attained for binary prediction.

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cs.LG cs.DS cs.GT stat.ML

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Simina Brânzei, Yuval Peres. 2018-07-30. Online Learning with an Almost Perfect Expert. https://doi.org/10.1073/pnas.1818908116

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