Online Classification with Predictions
Keywords: Online Learning, Algorithms with Predictions
TL;DR: We show that having access to good predictions about future examples can lead to better regret bounds in online classification.
Abstract: We study online classification when the learner has access to predictions about future examples. We design an online learner whose expected regret is never worse than the worst-case regret, gracefully improves with the quality of the predictions, and can be significantly better than the worst-case regret when the predictions of future examples are accurate. As a corollary, we show that if the learner is always guaranteed to observe data where future examples are easily predictable, then online learning can be as easy as transductive online learning. Our results complement recent work in online algorithms with predictions and smoothed online classification, which go beyond a worse-case analysis by using machine-learned predictions and distributional assumptions respectively.
Primary Area: Online learning
Submission Number: 4694
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