On Theorem 2.3 in "Prediction, Learning, and Games" by Cesa-Bianchi and Lugosi
classification
💻 cs.LG
keywords
numberalgorithmaverageboundcesa-bianchiexpertsexponentiallyforecaster
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The note presents a modified proof of a loss bound for the exponentially weighted average forecaster with time-varying potential. The regret term of the algorithm is upper-bounded by sqrt{n ln(N)} (uniformly in n), where N is the number of experts and n is the number of steps.
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