We show that learning algorithms satisfying a low approximate regret property experience fast convergence to approximate optimality in a large class of repeated games. Our property, which simply requires that each learner has small regret compared to a (1+eps)-multiplicative approximation to the best action in hindsight, is ubiquitous among learning algorithms; it is satisfied even by the vanilla Hedge forecaster. Our results improve upon recent work of Syrgkanis et al. in a number of ways. We require only that players observe payoffs under other players' realized actions, as opposed to expected payoffs. We further show that convergence occurs with high probability, and show convergence under bandit feedback.

Hence, a "fair" reward allocation scheme is desirable to give all parties enough incentives to join the collaboration.

We consider two versions: sequential, where Bob observes Alice's cut

The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model class one optimizes over--and the more data one has access to--the more one can improve performance. As models get deployed in a variety of real-world scenarios, they inevitably face strategic environments.

We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/