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We introduce Explearn, an online algorithm that learns to jointly outputpredictions and explanations for those predictions. Explearn leverages GaussianProcesses (GP)-based contextual bandits. This brings two key benefits. First,GPs naturally capture different kinds of explanations and enable the systemdesigner to control how explanations generalize across the space by virtue ofchoosing a suitable kernel. Second, Explearn builds on recent results incontextual bandits which guarantee convergence with high probability. Ourinitial experiments hint at the promise of the approach.

Bandits for Learning to Explain from Explanations