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This paper introduces a unified framework using surrogate losses to derive gradient-variation bounds for both non-stationary and universal online learning settings, simplifying prior disparate techniques. By relaxing extended smoothness assumptions and applying the method to online games, it advances theoretical guarantees in dynamic regret minimization without relying on specific hardware or infrastructure.
A Simple and Unified Method for Gradient-Variation Regret
arXiv:2610.26473v1 Announce Type: new Abstract: Gradient-variation online learning has attracted increasing attention for its connections to game theory and optimization. It has been studied in both non-stationary online learning, which aims to minimize dynamic regret against time-varying comparator sequences, and universal online learning, which seeks to adapt to unknown loss curvature. Prior work obtained gradient-variation bounds using different techniques tailored to each setting. We propose a simple and unified method based on surrogate losses with a quadratic penalty on decision movement, yielding gradient-variation bounds in both settings. For non-stationary online learning, the method induces an optimistic-to-gradient-variation reduction, enabling a broad class of algorithms to match the rate established by Zhao et al. (2024). For universal online learning, it yields a simple algorithm that matches the optimal universal regret bounds of Yan et al. (2024) while relaxing the extended smoothness assumption. Finally, we apply our method to online games and improve on existing guarantees.
Abstract:Gradient-variation online learning has attracted increasing attention for its connections to game theory and optimization. It has been studied in both non-stationary online learning, which aims to minimize dynamic regret against time-varying comparator sequences, and universal online learning, which seeks to adapt to unknown loss curvature. Prior work obtained gradient-variation bounds using different techniques tailored to each setting. We propose a simple and unified method based on surrogate losses with a quadratic penalty on decision movement, yielding gradient-variation bounds in both settings. For non-stationary online learning, the method induces an optimistic-to-gradient-variation reduction, enabling a broad class of algorithms to match the rate established by Zhao et al. (2024). For universal online learning, it yields a simple algorithm that matches the optimal universal regret bounds of Yan et al. (2024) while relaxing the extended smoothness assumption. Finally, we apply our method to online games and improve on existing guarantees.
Machine Learning (cs.LG); Optimization and Control (math.OC)
arXiv:2610.26473 [cs.LG]
(or arXiv:2610.26473v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.26473 Focus to learn more arXiv-issued DOI via DataCite
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