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A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk Minimization.

, , и . CoRR, (2019)

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Ching-hua Lee

Kong-Ching Lee

Ching-Lung Lee

Wilhelm Ching

Paisal Chingduang

 

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Manifold Identification for Ultimately Communication-Efficient Distributed Optimization., , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 5842-5852. PMLR, (2020)A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization., , и . KDD, стр. 1646-1655. ACM, (2018)Limited-memory Common-directions Method for Distributed Ll-regularized Linear Classification., , , и . SDM, стр. 504-512. SIAM, (2018)Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning., , , и . ICML, OpenReview.net, (2024)The Common-directions Method for Regularized Empirical Risk Minimization., , и . J. Mach. Learn. Res., (2019)Accelerating inexact successive quadratic approximation for regularized optimization through manifold identification.. Math. Program., 201 (1): 599-633 (2023)Relationship Quality and Customer Loyalty in Taiwan - A Longitudinal Aspect., , , и . IMIS, стр. 773-775. IEEE Computer Society, (2013)First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems., и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 3754-3762. PMLR, (2019)Training Structured Neural Networks Through Manifold Identification and Variance Reduction., и . ICLR, OpenReview.net, (2022)Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM., и . ICML, том 37 из JMLR Workshop and Conference Proceedings, стр. 987-996. JMLR.org, (2015)