Flaspohler, GenevieveOrabona, FrancescoCohen, JudahMouatadid, SoukaynaOprescu, MirunaOrenstein, PauloMackey, Lester2022-07-082022-07-082021G. Flaspohler, F. Orabona, J. Cohen, S. Mouatadid, M. Oprescu, P. Orenstein, L. Mackey. 2021. "Online Learning with Optimism and Delay." INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139.2640-3498https://hdl.handle.net/2144/44851Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback. Our algorithms—DORM, DORM+, and AdaHedgeD—arise from a novel reduction of delayed online learning to optimistic online learning that reveals how optimistic hints can mitigate the regret penalty caused by delay. We pair this delay-as-optimism perspective with a new analysis of optimistic learning that exposes its robustness to hinting errors and a new metaalgorithm for learning effective hinting strategies in the presence of delay. We conclude by benchmarking our algorithms on four subseasonal climate forecasting tasks, demonstrating low regret relative to state-of-the-art forecasting models.en-USConference materials643997