In recent years, the prevalence of large-scale data-sets and the demand for sophisti-cated learning models have necessitated the development of efficient distributed ma-chine learning (ML) solutions. Convergence speed is a critical factor influencing the practicality and effectiveness of these distributed frameworks.
arXiv:2607. 27632v1 Announce Type: new Abstract: With the rapid advancement of the Internet of Things (IoT), massive amounts of data are generated across distributed edge networks.
By Yang Jiao (Richard), Kaixuan Jiao (Richard), Kai Yang (Richard), Nadjib Aitsaadi (Richard), Ilhem Fajjari (Richard), Renwei (Richard), Li
arXiv:2405. 11667v2 Announce Type: replace Abstract: Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD.
By Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro
arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.
By Grigory Malinovsky
arXiv:2508. 00775v2 Announce Type: replace-cross Abstract: The design of many classical optimization algorithms is driven by the certification of linear convergence rates over classes of optimization problems.
By Andrea Martin, Ian R. Manchester, Luca Furieri
arXiv:2307. 05213v3 Announce Type: replace-cross Abstract: Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of information (e.
By Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali \.Irfan Mahmuto\u{g}ullar{\i}, Brandon Amos, Tias Guns, Michele Lombardi
arXiv:2502. 00753v4 Announce Type: replace-cross Abstract: Smoothness is crucial for attaining fast rates in first-order optimization.
By Dingzhi Yu, Wei Jiang, Hongyi Tao, Yuanyu Wan, Lijun Zhang
arXiv:2608. 09523v1 Announce Type: new Abstract: Deep neural network (DNN) training with stochastic gradient descent (SGD) and its variants achieves strong empirical performance, yet classical optimization theory does not fully explain this success.
By Binchuan Qi
arXiv:2310. 15976v4 Announce Type: replace Abstract: signSGD is attractive in nonconvex optimization because it communicates sign-valued rather than full-precision gradients.
By Zhen Qin, Zhishuai Liu, Pan Xu
arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.
By Rahul Vaze
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
The paper introduces Curvature-Scaled Sharpness-Aware Minimization (CS‑SAM), a variant of SAM that scales both the perturbation and descent step by curvature bounds to uniformly control gradient distortion under generalized smoothness conditions. It proves deterministic ≥1/T convergence rates for nonconvex objectives, last‑iterate ≥1/T rates for convex objectives, and linear convergence under the Polyak‑Łojasiewicz condition, while recovering standard gradient descent rates when the perturbation is zero. Experiments on computer vision tasks show that CS‑SAM, particularly the (H0,H1) variant, achieves the highest mean best‑test accuracy across four settings compared to tuned SAM and USAM.
By Dimitris Oikonomou, Nicolas Loizou