arXiv:2602.08372v2 Announce Type: replace
Abstract: We study dynamic regret minimization in non-stationary online learning, with a primary focus on follow-the-regularized-leader (FTRL) methods. FTRL...
By Yan-Feng Xie, Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou
arXiv:2601.13519v4 Announce Type: replace-cross
Abstract: This paper introduces a new problem-dependent regret measure for online convex optimization with smooth losses. The notion, which we call the...
By Wenzhi Gao, Chang He, Madeleine Udell
arXiv:2605. 17269v2 Announce Type: replace Abstract: This work introduces a general framework for calibeating based on regret minimization.
By Maximilian Fichtl, Crist\'obal Guzm\'an, Nishant A. Mehta
arXiv:2604. 11151v2 Announce Type: replace Abstract: We develop parameter-free algorithms for unconstrained online learning with regret guarantees that scale with the gradient variation $V_T(u) = \sum_{t=2}^T \|\nabla f_t(u)-\nabla f_{t-1}(u)\|^2$.
By Yuheng Zhao, Andrew Jacobsen, Nicol\`o Cesa-Bianchi, Peng Zhao
arXiv:2505. 21460v2 Announce Type: replace Abstract: We study online calibration of multi-dimensional forecasts over an arbitrary convex set $P \subset \mathbb{R}^d$ relative to an arbitrary norm $|\cdot|$.
By Maxwell Fishelson, Noah Golowich, Mehryar Mohri, Jon Schneider
arXiv:2608. 06656v1 Announce Type: new Abstract: Can one forecaster attain the optimal regret rate for every bounded proper loss and also adapt to every smooth proper loss?
By Pahan Dewasurendra