Lee Ching-pei

Global Convergence and Acceleration of Projection Methods for Feasibility Problems Involving Union Convex Sets. 

Journal of Optimization Theory and Applications 204(2) 19-19 2025年2月  最終著者

Regularized adaptive momentum dual averaging with an efficient inexact subproblem solver for training structured neural network 

Neural Information Processing Systems (NeurIPS) 37 128489-128515 2024年12月  査読有り最終著者責任著者

Accelerating inexact successive quadratic approximation for regularized optimization through manifold identification 

Mathematical Programming 2023年  査読有り筆頭著者最終著者責任著者

Accelerated projected gradient algorithms for sparsity constrained optimization problems 

Conference on Neural Information Processing Systems (NeurIPS) 2022年12月  査読有り最終著者責任著者

Training Structured Neural Networks Through Manifold Identification and Variance Reduction. 

International Conference on Learning Representations (ICLR) 2022年4月  査読有り最終著者責任著者

Limited-memory common-directions method for large-scale optimization: convergence, parallelization, and distributed optimization. 

Mathematical Programming Computation 14(3) 543-591 2022年  査読有り筆頭著者責任著者

Analyzing random permutations for cyclic coordinate descent. 

Mathematics of Computation 89(325) 2217-2248 2020年  査読有り

Inexact Variable Metric Stochastic Block-Coordinate Descent for Regularized Optimization. 

Journal of Optimization Theory and Applications 185(1) 151-187 2020年  査読有り筆頭著者責任著者

Manifold Identification for Ultimately Communication-Efficient Distributed Optimization. 

Proceedings of the 37th International Conference on Machine Learning (ICML) 5842-5852 2020年  査読有り最終著者責任著者

Random permutations fix a worst case for cyclic coordinate descent 

IMA Journal of Numerical Analysis 39(3) 1246-1275 2019年7月1日  査読有り筆頭著者

First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems. 

Proceedings of the 36th International Conference on Machine Learning (ICML) 3754-3762 2019年  査読有り筆頭著者責任著者

Inexact Successive quadratic approximation for regularized optimization. 

Computational Optimization and Applications 72(3) 641-674 2019年  査読有り筆頭著者責任著者

The Common-directions Method for Regularized Empirical Risk Minimization. 

Journal of Machine Learning Research (JMLR) 20 58-49 2019年  査読有り責任著者

A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization. 

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) 1646-1655 2018年8月  査読有り筆頭著者責任著者

Limited-memory Common-directions Method for Distributed Optimization and its Application on Empirical Risk Minimization. 

Proceedings of the 2017 SIAM International Conference on Data Mining 732-740 2017年  査読有り筆頭著者責任著者