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cs.LG2025
Adaptivity and Universality: Problem-dependent Universal Regret for Online Convex Optimization
Peng Zhao, Yu-Hu Yan, Hang Yu +1
Universal online learning aims to achieve optimal regret guarantees without requiring prior knowledge of the curvature of online functions. Existing methods have established minima…
cs.LG2025
Optimistic Online-to-Batch Conversions for Accelerated Convergence and Universality
Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou
In this work, we study offline convex optimization with smooth objectives, where the classical Nesterov's Accelerated Gradient (NAG) method achieves the optimal accelerated converg…
cs.LG2025
Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness
Yuheng Zhao, Yu-Hu Yan, Kfir Yehuda Levy +1
Smoothness is known to be crucial for acceleration in offline optimization, and for gradient-variation regret minimization in online learning. Interestingly, these two problems are…