Online Adaptation to Label Distribution Shift. (arXiv:2107.04520v2 [cs.LG] UPDATED)

Machine learning models often encounter distribution shifts when deployed in
the real world. In this paper, we focus on adaptation to label distribution
shift in the online setting, where the test-time label distribution is
continually changing and the model must dynamically adapt to it without
observing the true label. Leveraging a novel analysis, we show that the lack of
true label does not hinder estimation of the expected test loss, which enables
the reduction of online label shift adaptation to …

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