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arXiv · 1909.05007

Optimality of the Subgradient Algorithm in the Stochastic Setting

Daron Anderson·Douglas Leith
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Abstract

We show that the Subgradient algorithm is universal for online learning on the simplex in the sense that it simultaneously achieves $O(\sqrt N)$ regret for adversarial costs and $O(1)$ pseudo-regret for i.i.d costs. To the best of our knowledge this is the first demonstration of a universal algorithm on the simplex that is not a variant of Hedge. Since Subgradient is a popular and widely used algorithm our results have immediate broad application.

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BibTeXRIS

Daron Anderson, Douglas Leith. 2019-09-10. Optimality of the Subgradient Algorithm in the Stochastic Setting. https://arxiv.org/abs/1909.05007

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