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Online Submodular Set Cover, Ranking, and Repeated Active Learning
Andrew Guillory · Jeff Bilmes

Tue Dec 13th 01:38 -- 01:42 PM @ None

We propose an online prediction version of submodular set cover with connections to ranking and repeated active learning. In each round, the learning algorithm chooses a sequence of items. The algorithm then receives a monotone submodular function and suffers loss equal to the cover time of the function: the number of items needed, when items are selected in order of the chosen sequence, to achieve a coverage constraint. We develop an online learning algorithm whose loss converges to approximately that of the best sequence in hindsight. Our proposed algorithm is readily extended to a setting where multiple functions are revealed at each round and to bandit and contextual bandit settings.

Author Information

Andrew Guillory (University of Washington)
Jeff Bilmes (University of Washington, Seattle)

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