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