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2020 – today
- 2024
[c27]Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low:
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. AAAI 2024: 14097-14105- [c26]Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo:
Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization. AAAI 2024: 17477-17486 - [c25]Lesi Chen, Haishan Ye, Luo Luo:
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. AISTATS 2024: 1990-1998 - [c24]Yunyan Bai, Yuxing Liu, Luo Luo:
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition. ICML 2024 - [c23]Yuxing Liu, Lesi Chen, Luo Luo:
Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers. ICML 2024 - [c22]Zhuanghua Liu, Cheng Chen, Luo Luo, Bryan Kian Hsiang Low:
Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization. ICML 2024 - [i27]Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low:
Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. CoRR abs/2402.02359 (2024) - [i26]Yunyan Bai, Yuxing Liu, Luo Luo:
On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition. CoRR abs/2402.02569 (2024) - [i25]Qihao Zhou, Haishan Ye, Luo Luo:
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. CoRR abs/2405.16126 (2024) - [i24]Zhiling Zhou, Zhuanghua Liu, Chengchang Liu, Luo Luo:
Incremental Gauss-Newton Methods with Superlinear Convergence Rates. CoRR abs/2407.03195 (2024) - 2023
- [j7]Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang:
Multi-Consensus Decentralized Accelerated Gradient Descent. J. Mach. Learn. Res. 24: 306:1-306:50 (2023) - [c21]Lesi Chen, Jing Xu, Luo Luo:
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. ICML 2023: 5219-5233 - [c20]Chengchang Liu
, Lesi Chen, Luo Luo, John C. S. Lui:
Communication Efficient Distributed Newton Method with Fast Convergence Rates. KDD 2023: 1406-1416 - [c19]Chengchang Liu, Cheng Chen, Luo Luo, John C. S. Lui:
Block Broyden's Methods for Solving Nonlinear Equations. NeurIPS 2023 - [i23]Lesi Chen, Jing Xu, Luo Luo:
Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. CoRR abs/2301.06428 (2023) - [i22]Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan:
Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR abs/2307.00126 (2023) - [i21]Lesi Chen, Boyuan Yao, Luo Luo:
Faster Stochastic Algorithms for Minimax Optimization under Polyak-Łojasiewicz Conditions. CoRR abs/2307.15868 (2023) - [i20]Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo:
Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization. CoRR abs/2310.11973 (2023) - 2022
- [c18]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: 1031-1041 - [c17]Lesi Chen, Boyuan Yao, Luo Luo:
Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition. NeurIPS 2022 - [c16]Chengchang Liu, Luo Luo:
Quasi-Newton Methods for Saddle Point Problems. NeurIPS 2022 - [c15]Luo Luo, Yujun Li, Cheng Chen:
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization. NeurIPS 2022 - [i19]Luo Luo, Haishan Ye:
Decentralized Stochastic Variance Reduced Extragradient Method. CoRR abs/2202.00509 (2022) - [i18]Lesi Chen, Luo Luo:
Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization. CoRR abs/2208.05925 (2022) - [i17]Luo Luo, Haishan Ye:
An Optimal Stochastic Algorithm for Decentralized Nonconvex Finite-sum Optimization. CoRR abs/2210.13931 (2022) - [i16]Lesi Chen, Haishan Ye, Luo Luo:
A Simple and Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. CoRR abs/2212.02387 (2022) - 2021
- [j6]Haishan Ye, Luo Luo, Zhihua Zhang:
Approximate Newton Methods. J. Mach. Learn. Res. 22: 66:1-66:41 (2021) - [j5]Haishan Ye, Luo Luo, Zhihua Zhang:
Accelerated Proximal Subsampled Newton Method. IEEE Trans. Neural Networks Learn. Syst. 32(10): 4374-4388 (2021) - [c14]Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye:
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. AAAI 2021: 8793-8800 - [i15]Luo Luo, Guangzeng Xie, Tong Zhang, Zhihua Zhang:
Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization. CoRR abs/2106.01761 (2021) - [i14]Luo Luo, Cheng Chen:
Finding Second-Order Stationary Point for Nonconvex-Strongly-Concave Minimax Problem. CoRR abs/2110.04814 (2021) - [i13]Chengchang Liu, Luo Luo:
Quasi-Newton Methods for Saddle Point Problems and Beyond. CoRR abs/2111.02708 (2021) - 2020
- [j4]Haishan Ye, Luo Luo, Zhihua Zhang:
Nesterov's Acceleration for Approximate Newton. J. Mach. Learn. Res. 21: 142:1-142:37 (2020) - [c13]Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang:
Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. ICML 2020: 10504-10513 - [c12]Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu, Yijiang Lian:
Efficient and Robust High-Dimensional Linear Contextual Bandits. IJCAI 2020: 4259-4265 - [c11]Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu:
Efficient Projection-free Algorithms for Saddle Point Problems. NeurIPS 2020 - [c10]Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang:
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. NeurIPS 2020 - [c9]Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang:
Decentralized Accelerated Proximal Gradient Descent. NeurIPS 2020 - [i12]Luo Luo, Haishan Ye, Tong Zhang:
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. CoRR abs/2001.03724 (2020) - [i11]Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang:
Multi-consensus Decentralized Accelerated Gradient Descent. CoRR abs/2005.00797 (2020) - [i10]Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye:
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. CoRR abs/2009.02553 (2020) - [i9]Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu:
Efficient Projection-Free Algorithms for Saddle Point Problems. CoRR abs/2010.11737 (2020)
2010 – 2019
- 2019
- [j3]Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang:
Robust Frequent Directions with Application in Online Learning. J. Mach. Learn. Res. 20: 45:1-45:41 (2019) - [j2]Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang:
Fast stochastic second-order method logarithmic in condition number. Pattern Recognit. 88: 629-642 (2019) - [i8]Guangzeng Xie, Luo Luo, Zhihua Zhang:
A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization. CoRR abs/1908.08394 (2019) - [i7]Luo Luo, Cheng Chen, Yujun Li, Guangzeng Xie, Zhihua Zhang:
A Stochastic Proximal Point Algorithm for Saddle-Point Problems. CoRR abs/1909.06946 (2019) - 2018
- [c8]Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, Jian Pei:
Sketched Follow-The-Regularized-Leader for Online Factorization Machine. KDD 2018: 1900-1909 - 2017
- [c7]Zihao Chen, Luo Luo, Zhihua Zhang:
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. AAAI 2017: 1812-1818 - [c6]Haishan Ye, Luo Luo, Zhihua Zhang:
Approximate Newton Methods and Their Local Convergence. ICML 2017: 3931-3939 - [i6]Haishan Ye, Luo Luo, Zhihua Zhang:
A Unifying Framework for Convergence Analysis of Approximate Newton Methods. CoRR abs/1702.08124 (2017) - [i5]Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li:
Online Learning Via Regularized Frequent Directions. CoRR abs/1705.05067 (2017) - 2016
- [j1]Shusen Wang, Luo Luo, Zhihua Zhang:
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions. J. Mach. Learn. Res. 17: 49:1-49:49 (2016) - [c5]Qiaomin Ye, Luo Luo, Zhihua Zhang:
Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA. IJCAI 2016: 2301-2307 - [c4]Tianfan Fu, Luo Luo, Zhihua Zhang:
Quasi-Newton Hamiltonian Monte Carlo. UAI 2016 - [i4]Luo Luo, Zihao Chen, Zhihua Zhang, Wu-Jun Li:
Variance-Reduced Second-Order Methods. CoRR abs/1602.00223 (2016) - [i3]Haishan Ye, Luo Luo, Zhihua Zhang:
Revisiting Sub-sampled Newton Methods. CoRR abs/1608.02875 (2016) - [i2]Zihao Chen, Luo Luo, Zhihua Zhang:
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. CoRR abs/1612.00599 (2016) - 2015
- [c3]Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li:
Support Matrix Machines. ICML 2015: 938-947 - 2014
- [i1]Shusen Wang, Luo Luo, Zhihua Zhang:
The Modified Nystrom Method: Theories, Algorithms, and Extension. CoRR abs/1406.5675 (2014) - 2013
- [c2]Zhiquan Liu, Luo Luo, Wu-Jun Li:
Robust crowdsourced learning. IEEE BigData 2013: 338-343
2000 – 2009
- 2008
- [c1]Joanne McGrath Cohoon, Zhen Wu, Luo Luo:
Will they stay or will they go? SIGCSE 2008: 397-401
Coauthor Index
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