Active Learning under Label Shift
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Distribution shift poses a challenge for active data collection in the real world. We address the problem of active learning under label shift and propose ALLS, the first framework for active learning under label shift. ALLS builds on label shift estimation techniques to correct for label shift with a balance of importance weighting and class-balanced sampling. We show a bias-variance trade-off between these two techniques and prove error and sample complexity bounds for a disagreement-based algorithm under ALLS. Experiments across a range of label shift settings demonstrate ALLS consistently improves performance, often reducing sample complexity by more than half an order of magnitude. Ablation studies corroborate the bias-variance trade-off revealed by our theory
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