Online Submodular Set Cover, Ranking, and Repeated Active Learning

author: Jeff A. Bilmes, Department of Electrical Engineering, University of Washington
published: Sept. 6, 2012,   recorded: December 2011,   views: 2511
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Description

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.

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Download slides icon Download slides: nips2011_bilmes_repeated_01.pdf (302.7 KB)

Download article icon Download article: nips2011_0663.pdf (352.2 KB)


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