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cs.LG2025
Dual Adaptivity: Universal Algorithms for Minimizing the Adaptive Regret of Convex Functions
Lijun Zhang, Wenhao Yang, Guanghui Wang +2
To deal with changing environments, a new performance measure -- adaptive regret, defined as the maximum static regret over any interval, was proposed in online learning. Under the…
math.OC2025
Better Convergence Guarantees for Sign-Based Momentum Methods
Wei Jiang, Dingzhi Yu, Sifan Yang +3
This paper presents an improved analysis for sign-based methods with momentum updates. Traditional sign-based methods obtain a convergence rate of under the…
cs.LG2025
Discounted Online Convex Optimization: Uniform Regret Across a Continuous Interval
Wenhao Yang, Sifan Yang, Lijun Zhang
Reflecting the greater significance of recent history over the distant past in non-stationary environments, -discounted regret has been introduced in online convex optimization…