Theory and Algorithm for Batch Distribution Drift ProblemsDownload PDF

05 Oct 2022, 00:13 (modified: 21 Oct 2022, 17:31)NeurIPS 2022 Workshop DistShift PosterReaders: Everyone
Keywords: concept drift, drifting, generalization bounds, drifting algorithm, drifting experiments
Abstract: We study a problem of gradual \emph{batch distribution drift} motivated by several applications, which consists of determining an accurate predictor for a target time segment, for which a moderate amount of labeled samples are at one's disposal, while leveraging past segments for which substantially more labeled samples are available. We give new algorithms for this problem guided by a new theoretical analysis and generalization bounds derived for this scenario. Additionally, we report the results of extensive experiments demonstrating the benefits of our drifting algorithm, including comparisons with natural baselines.
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