We study the selective learning problem introduced by Qiao and Valiant (2019), in which the learner observes $n$ labeled data points one at a time. At a time of its choosing, the learner selects a window length $w$ and a model $hatell$ from the model class $mathcal{L}$, and then labels the next $w$ data points using $hatell$. The excess risk incurred by the learner is defined as the difference between the average loss of $hatell$ over those $w$ data points and the smallest possible average loss among all models in $mathcal{L}$ over those $w$ data points. We give an improved algorithm, termed the hybrid exponential weights algorithm, that achieves an expected excess risk of $O((loglog|mathcal{L}| + loglog n)/log n)$. This result gives a doubly exponential improvement in the dependence on $|mathcal{L}|$ over the best known bound of $O(sqrt{|mathcal{L}|/log n})$. We complement the positive result with an almost matching lower bound, which suggests the worst-case optimality of the algorithm. We also study a more restrictive family of learning algorithms that are bounded-recall in the sense that when a prediction window of length $w$ is chosen, the learners decision only depends on the most recent $w$ data points. We analyze an exponential weights variant of the ERM algorithm in Qiao and Valiant (2019). This new algorithm achieves an expected excess risk of $O(sqrt{log |mathcal{L}|/log n})$, which is shown to be nearly optimal among all bounded-recall learners. Our analysis builds on a generalized version of the selective mean prediction problem in Drucker (2013); Qiao and Valiant (2019), which may be of independent interest.
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