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Active Learning with a Drifting Distribution
Liu Yang

Mon Dec 12 10:00 AM -- 02:59 PM (PST) @

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.

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Liu Yang (CMU)

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