Why it matters

Multiplicative weights unifies many algorithms. Understanding shapes ML theory.

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The architecture

Distribution over experts.

Losses observed per expert.

Weights *= exp(-eta * loss).

Renormalize.

Multiplicative weightsExpert weightsdistributionObserve lossesper expertUpdateweight *= exp(-eta * loss)AdaBoost + Hedge + zero-sum games + LP + more all fit MW framework
Multiplicative weights.
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How it works end to end

Arora-Hazan-Kale survey.

AdaBoost + Hedge.

LP + zero-sum games.

Regret O(sqrt(T log n)).