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Electronic Colloquium on Computational Complexity

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Reports tagged with regret:
TR07-088 | 7th September 2007
Elad Hazan, C. Seshadhri

Adaptive Algorithms for Online Decision Problems

Revisions: 1

We study the notion of learning in an oblivious changing environment. Existing online learning algorithms which minimize regret are shown to converge to the average of all locally optimal solutions. We propose a new performance metric, strengthening the standard metric of regret, to capture convergence to locally optimal solutions, and ... more >>>

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