Online Learning with Optimism and Delay
Genevieve E Flaspohler,u00a0Francesco Orabona,u00a0Judah Cohen,u00a0Soukayna Mouatadid,u00a0Miruna Oprescu,u00a0Paulo Orenstein,u00a0Lester Mackey
Inspired 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 algorithmsu2014DORM, DORM+, and AdaHedgeDu2014arise 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 meta-algorithm 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.
