Luo Luo

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 54

Top Topics

Optimization Problems

Top Venues

J. Mach. Learn. Res.

Publications

Yunyan Bai, Yuxing Liu, Luo Luo
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition. CoRR (2024)
Lesi Chen, Haishan Ye, Luo Luo
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. AISTATS (2024)
Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo
Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization. AAAI (2024)
Qihao Zhou, Haishan Ye, Luo Luo
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. CoRR (2024)
Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. CoRR (2024)
Zhiling Zhou, Zhuanghua Liu, Chengchang Liu, Luo Luo
Incremental Gauss-Newton Methods with Superlinear Convergence Rates. CoRR (2024)
Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. AAAI (2024)
Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo
Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization. CoRR (2023)
Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan
Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR (2023)
Lesi Chen, Boyuan Yao, Luo Luo
Faster Stochastic Algorithms for Minimax Optimization under Polyak-Łojasiewicz Conditions. CoRR (2023)
Lesi Chen, Jing Xu, Luo Luo
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. CoRR (2023)
Chengchang Liu, Lesi Chen, Luo Luo, John C. S. Lui
Communication Efficient Distributed Newton Method with Fast Convergence Rates. KDD (2023)
Chengchang Liu, Cheng Chen, Luo Luo, John C. S. Lui
Block Broyden's Methods for Solving Nonlinear Equations. NeurIPS (2023)
Lesi Chen, Jing Xu, Luo Luo
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. ICML (2023)
Chengchang Liu, Shuxian Bi, Luo Luo, John C. S. Lui
Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality. KDD (2022)
Luo Luo, Haishan Ye
Decentralized Stochastic Variance Reduced Extragradient Method. CoRR (2022)
Lesi Chen, Luo Luo
Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization. CoRR (2022)
Lesi Chen, Boyuan Yao, Luo Luo
Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition. NeurIPS (2022)
Luo Luo, Yujun Li, Cheng Chen
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization. NeurIPS (2022)
Chengchang Liu, Luo Luo
Quasi-Newton Methods for Saddle Point Problems. NeurIPS (2022)
Lesi Chen, Haishan Ye, Luo Luo
A Simple and Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. CoRR (2022)
Luo Luo, Haishan Ye
An Optimal Stochastic Algorithm for Decentralized Nonconvex Finite-sum Optimization. CoRR (2022)
Luo Luo, Guangzeng Xie, Tong Zhang, Zhihua Zhang
Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization. CoRR (2021)
Luo Luo, Cheng Chen
Finding Second-Order Stationary Point for Nonconvex-Strongly-Concave Minimax Problem. CoRR (2021)
Chengchang Liu, Luo Luo
Quasi-Newton Methods for Saddle Point Problems and Beyond. CoRR (2021)
Haishan Ye, Luo Luo, Zhihua Zhang
Accelerated Proximal Subsampled Newton Method. IEEE Trans. Neural Networks Learn. Syst. 32 (10) (2021)
Haishan Ye, Luo Luo, Zhihua Zhang
Approximate Newton Methods. J. Mach. Learn. Res. 22 (2021)
Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. AAAI (2021)
Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang
Multi-consensus Decentralized Accelerated Gradient Descent. CoRR (2020)
Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. NeurIPS (2020)
Luo Luo, Haishan Ye, Tong Zhang
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. CoRR (2020)
Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu
Efficient Projection-Free Algorithms for Saddle Point Problems. CoRR (2020)
Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. CoRR (2020)
Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang
Decentralized Accelerated Proximal Gradient Descent. NeurIPS (2020)
Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu, Yijiang Lian
Efficient and Robust High-Dimensional Linear Contextual Bandits. IJCAI (2020)
Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. ICML (2020)
Haishan Ye, Luo Luo, Zhihua Zhang
Nesterov's Acceleration for Approximate Newton. J. Mach. Learn. Res. 21 (2020)
Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu
Efficient Projection-free Algorithms for Saddle Point Problems. NeurIPS (2020)
Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang
Fast stochastic second-order method logarithmic in condition number. Pattern Recognit. 88 (2019)
Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang
Robust Frequent Directions with Application in Online Learning. J. Mach. Learn. Res. 20 (2019)
Luo Luo, Cheng Chen, Yujun Li, Guangzeng Xie, Zhihua Zhang
A Stochastic Proximal Point Algorithm for Saddle-Point Problems. CoRR (2019)
Guangzeng Xie, Luo Luo, Zhihua Zhang
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization. CoRR (2019)
Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, Jian Pei
Sketched Follow-The-Regularized-Leader for Online Factorization Machine. KDD (2018)
Haishan Ye, Luo Luo, Zhihua Zhang
A Unifying Framework for Convergence Analysis of Approximate Newton Methods. CoRR (2017)
Haishan Ye, Luo Luo, Zhihua Zhang
Approximate Newton Methods and Their Local Convergence. ICML (2017)
Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li
Online Learning Via Regularized Frequent Directions. CoRR (2017)
Zihao Chen, Luo Luo, Zhihua Zhang
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. AAAI (2017)
Qiaomin Ye, Luo Luo, Zhihua Zhang
Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA. IJCAI (2016)
Luo Luo, Zihao Chen, Zhihua Zhang, Wu-Jun Li
Variance-Reduced Second-Order Methods. CoRR (2016)
Haishan Ye, Luo Luo, Zhihua Zhang
Revisiting Sub-sampled Newton Methods. CoRR (2016)
Zihao Chen, Luo Luo, Zhihua Zhang
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. CoRR (2016)
Tianfan Fu, Luo Luo, Zhihua Zhang
Quasi-Newton Hamiltonian Monte Carlo. UAI (2016)
Shusen Wang, Luo Luo, Zhihua Zhang
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions. J. Mach. Learn. Res. 17 (2016)
Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li
Support Matrix Machines. ICML (2015)
Shusen Wang, Luo Luo, Zhihua Zhang
The Modified Nystrom Method: Theories, Algorithms, and Extension. CoRR (2014)
Zhiquan Liu, Luo Luo, Wu-Jun Li
Robust crowdsourced learning. IEEE BigData (2013)
Joanne McGrath Cohoon, Zhen Wu, Luo Luo
Will they stay or will they go? SIGCSE (2008)