arXiv Machine Learning By Yan-Feng Xie, Shuche Wang, Peng Zhao, Zhi-Hua Zhou

Online Learning with Gradient-Variation Interval Regret

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arXiv:2606. 03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval.

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arXiv Machine Learning
Jun 3

Data- and Variance-dependent Regret Bounds for Online Tabular MDPs

arXiv:2602. 01903v2 Announce Type: replace Abstract: This work studies online episodic tabular Markov decision processes (MDPs) with known transitions and develops best-of-both-worlds algorithms that achieve refined data-dependent regret bounds in the adversarial regime and variance-dependent regret bounds in the stochastic regime.

By Mingyi Li, Taira Tsuchiya, Kenji Yamanishi
arXiv Machine Learning
Sep 21

From Switching to Dynamic Regret: A Simple Reduction via Unbiased Random Sequences

The paper introduces a straightforward framework that transforms dynamic regret minimization into switching regret minimization by constructing an unbiased random sequence for any comparator sequence. Using this reduction, the authors derive dynamic regret bounds for strongly convex and exp-concave losses of “~O(T^{1/3}P_T^{2/3})” and for general convex losses of “O(√{T(1+P_T)})”, matching known minimax optimal results. The approach leverages off-the-shelf switching regret algorithms and controlled variance to achieve these bounds.

By Yibo Wang, Wenhao Yang, Sifan Yang, Yuanyu Wan, Lijun Zhang