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The Common-directions Method for Regularized Empirical Risk Minimization.

, , and . J. Mach. Learn. Res., (2019)

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Manifold Identification for Ultimately Communication-Efficient Distributed Optimization., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 5842-5852. PMLR, (2020)Limited-memory Common-directions Method for Distributed Ll-regularized Linear Classification., , , and . SDM, page 504-512. SIAM, (2018)A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization., , and . KDD, page 1646-1655. ACM, (2018)Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning., , , and . ICML, OpenReview.net, (2024)The Common-directions Method for Regularized Empirical Risk Minimization., , and . J. Mach. Learn. Res., (2019)First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems., and . ICML, volume 97 of Proceedings of Machine Learning Research, page 3754-3762. PMLR, (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., , , and . IMIS, page 773-775. IEEE Computer Society, (2013)Inexact Successive quadratic approximation for regularized optimization., and . Comput. Optim. Appl., 72 (3): 641-674 (2019)Distributed block-diagonal approximation methods for regularized empirical risk minimization., and . Mach. Learn., 109 (4): 813-852 (2020)