When facing uncertainty, decision-makers want predictions they can trust.

Then, we combine the results of these runs to obtain the final result. We prove that DropGNNs can distinguish various graph neighborhoods that cannot be separated by message passing GNNs.

We provide additional discussion about the related work in Appendix B. We provide the proof of our regret lower bound (Theorem 1) in Appendix C. We prove a concentration inequality for pairwise estimates in Appendix D. We then provide the proof of our regret upper bound (Theorem 2) in Appendix E. In Appendix F we provide additional details about our experimental setup. In Appendix G we provide experimental results for an alternate notion of regret. Appendix H contains some technical lemmas used in the proof of the upper bound result in Theorem 2. There has been some recent interest in bandit settings where more than two arms are played at once (although no previous work considers choice models at the level of generality we do). We review related work here and provide a summary in Table 1. Moreover, we study a much more general class of choice models than the MNL model studied by them.

Recent literature has made much progress in understanding online LQR: a modern learning-theoretic take on the classical control problem where a learner attempts to optimally control an unknown linear dynamical system with fully observed state, perturbed by i.i.d.

A key requirement is that the concepts be interpretable. Existing CBMs tackle this desideratum using a variety of heuristics based on unclear notions of interpretability, and fail to acquire concepts with the intended semantics.