We introduce TransferBench, a comprehensive benchmark for evaluating ensemble-based black-box adversarial attacks under realistic scenarios, revealing limitations in surrogate model choices, robustness generalization, and query efficiency.

We develop a general theory of agnostic online learning from continuous-time data streams under limited queries, providing tight regret bounds for both oblivious and adaptive settings.