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Title: Online Learning with Optimism and Delay
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 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 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.  more » « less
Award ID(s):
1908111 1925930 2022446
NSF-PAR ID:
10275615
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Internation conference on machine learning
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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