arXiv:2609.08380v1 Announce Type: cross
Abstract: We study the stochastic first-order oracle complexity for constrained or regularized convex-concave min-max optimization and stochastic monotone vari...
By Ahmet Alacaoglu
arXiv:2604. 03146v2 Announce Type: replace-cross Abstract: We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs.
By Chiheb Yaakoubi, Cosme Louart, Malik Tiomoko, Zhenyu Liao
arXiv:2606. 28573v1 Announce Type: new Abstract: Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions.
By Andrea Montanari, Kangjie Zhou
arXiv:2512.24999v2 Announce Type: replace-cross
Abstract: In this work, we introduce $\textit{basic inequalities}$ for first-order iterative optimization algorithms, forming a simple yet versatile fr...
By Seunghoon Paik, Kangjie Zhou, Matus Telgarsky, Ryan J. Tibshirani
arXiv:2509. 17251v2 Announce Type: replace-cross Abstract: Existing theory suggests that for linear regression problems categorized by capacity and source conditions, gradient descent (GD) is always minimax optimal, while both ridge regression and online stochastic gradient descent (SGD) are polynomially suboptimal for certain categories of such problems.
By Jingfeng Wu, Peter L. Bartlett, Sham M. Kakade, Jason D. Lee, Bin Yu
arXiv:2610.01662v1 Announce Type: cross
Abstract: We establish complexity lower bounds for stochastic first-order algorithms in nonconvex--concave minimax optimization, allowing algorithms to use var...
By Jiayi Song, Zi Xu