Active Learning with a Drifting Distribution

Part of Advances in Neural Information Processing Systems 24 (NIPS 2011)

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Liu Yang


We study the problem of active learning in a stream-based setting, allowing the distribution of the examples to change over time. We prove upper bounds on the number of prediction mistakes and number of label requests for established disagreement-based active learning algorithms, both in the realizable case and under Tsybakov noise. We further prove minimax lower bounds for this problem.