Yishay Mansour

We provided individual regret bounds for cooperative stochastic multi-armed bandits over communication graphs, independent of graph diameter, and also analyzed trade-offs with message size and communication rounds.

We present regret bounds for adversarial contextual bandits with general function approximation under delayed bandit feedback.

We propose the first Best-of-Both-Worlds algorithm for multi-armed bandits with adversarial delays that matches lower bounds in both stochastic and adversarial settings, significantly improving previous results.

We study learning set functions under one-sided feedback in the PAC learning framework.