Luo Luo - RankMe

82 papers A* 31A 3Journal 47Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Decentralized Non-convex Stochastic Optimization with Heterogeneous Variance. AAAI Hongxu Chen, Ke Wei, Luo Luo
2026	J	`jnl`	Decentralized Non-convex Stochastic Optimization with Heterogeneous Variance. CoRR Hongxu Chen, Ke Wei, Luo Luo
2026	J	`jnl`	Decentralized Nonsmooth Nonconvex Optimization with Client Sampling. CoRR Xinyan Chen, Weiguo Gao, Luo Luo
2026	J	`jnl`	Near-Optimal Decentralized Stochastic Nonconvex Optimization with Heavy-Tailed Noise. CoRR Menglian Wang, Zhuanghua Liu, Luo Luo
2026	J	`jnl`	On the Convergence of Single-Loop Stochastic Bilevel Optimization with Approximate Implicit Differentiation. CoRR Yubo Zhou, Luo Luo, Guang Dai, Haishan Ye
2026	J	`jnl`	Optimal Asynchronous Stochastic Nonconvex Optimization under Heavy-Tailed Noise. CoRR Yidong Wu, Luo Luo
2026	A*	`conf`	Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack. AAAI Jing Xue, Zhishen Sun, Haishan Ye, Luo Luo, Xiangyu Chang, Guang Dai
2026	J	`jnl`	Sign-Based Optimizers Are Effective Under Heavy-Tailed Noise. CoRR Dingzhi Yu, Hongyi Tao, Yuanyu Wan, Luo Luo, Lijun Zhang
2026	J	`jnl`	Stability and Generalization of Nonconvex Optimization with Heavy-Tailed Noise. CoRR Hongxu Chen, Ke Wei, Xiaoming Yuan, Luo Luo
2025	A*	`conf`	A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization. ICML Kunjie Ren, Luo Luo
2025	J	`jnl`	Accelerated Evolving Set Processes for Local PageRank Computation. CoRR Binbin Huang, Luo Luo, Yanghua Xiao, Deqing Yang, Baojian Zhou
2025	A*	`conf`	An Enhanced Levenberg-Marquardt Method via Gram Reduction. AAAI Chengchang Liu, Luo Luo, John C. S. Lui
2025	J	`jnl`	Decentralized Stochastic Nonconvex Optimization under the Relaxed Smoothness. CoRR Luo Luo, Xue Cui, Tingkai Jia, Cheng Chen
2025	J	`jnl`	Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack. CoRR Jing Xue, Zhishen Sun, Haishan Ye, Luo Luo, Xiangyu Chang, Ivor W. Tsang, Guang Dai
2025	A*	`conf`	Solving Convex-Concave Problems with 풪(ε COLT Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang
2025	J	`jnl`	Solving Convex-Concave Problems with 𝒪(ε CoRR Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang
2025	J	`jnl`	Stochastic Bilevel Optimization with Heavy-Tailed Noise. CoRR Zhuanghua Liu, Luo Luo
2024	A	`conf`	An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. AISTATS Lesi Chen, Haishan Ye, Luo Luo
2024	A*	`conf`	Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers. ICML Yuxing Liu, Lesi Chen, Luo Luo
2024	A*	`conf`	Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization. AAAI Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo
2024	A*	`conf`	Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization. NeurIPS Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
2024	J	`jnl`	Incremental Gauss-Newton Methods with Superlinear Convergence Rates. CoRR Zhiling Zhou, Zhuanghua Liu, Chengchang Liu, Luo Luo
2024	A*	`conf`	Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. AAAI Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
2024	J	`jnl`	Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. CoRR Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low
2024	J	`jnl`	Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization. J. Mach. Learn. Res. Lesi Chen, Luo Luo
2024	A*	`conf`	Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. NeurIPS Qihao Zhou, Haishan Ye, Luo Luo
2024	J	`jnl`	Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. CoRR Qihao Zhou, Haishan Ye, Luo Luo
2024	A*	`conf`	On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition. ICML Yunyan Bai, Yuxing Liu, Luo Luo
2024	J	`jnl`	On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition. CoRR Yunyan Bai, Yuxing Liu, Luo Luo
2024	A*	`conf`	Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition. NeurIPS Shihong Ding, Long Yang, Luo Luo, Cong Fang
2024	A*	`conf`	Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization. ICML Zhuanghua Liu, Cheng Chen, Luo Luo, Bryan Kian Hsiang Low
2023	J	`jnl`	Accelerating Inexact HyperGradient Descent for Bilevel Optimization. CoRR Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan
2023	A*	`conf`	Block Broyden's Methods for Solving Nonlinear Equations. NeurIPS Chengchang Liu, Cheng Chen, Luo Luo, John C. S. Lui
2023	A*	`conf`	Communication Efficient Distributed Newton Method with Fast Convergence Rates. KDD Chengchang Liu, Lesi Chen, Luo Luo, John C. S. Lui
2023	J	`jnl`	Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization. CoRR Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo
2023	A*	`conf`	Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. ICML Lesi Chen, Jing Xu, Luo Luo
2023	J	`jnl`	Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. CoRR Lesi Chen, Jing Xu, Luo Luo
2023	J	`jnl`	Faster Stochastic Algorithms for Minimax Optimization under Polyak-Łojasiewicz Conditions. CoRR Lesi Chen, Boyuan Yao, Luo Luo
2023	J	`jnl`	Multi-Consensus Decentralized Accelerated Gradient Descent. J. Mach. Learn. Res. Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang
2022	J	`jnl`	A Simple and Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. CoRR Lesi Chen, Haishan Ye, Luo Luo
2022	J	`jnl`	An Optimal Stochastic Algorithm for Decentralized Nonconvex Finite-sum Optimization. CoRR Luo Luo, Haishan Ye
2022	J	`jnl`	Decentralized Stochastic Variance Reduced Extragradient Method. CoRR Luo Luo, Haishan Ye
2022	A*	`conf`	Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition. NeurIPS Lesi Chen, Boyuan Yao, Luo Luo
2022	A*	`conf`	Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization. NeurIPS Luo Luo, Yujun Li, Cheng Chen
2022	J	`jnl`	Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization. CoRR Lesi Chen, Luo Luo
2022	A*	`conf`	Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality. KDD Chengchang Liu, Shuxian Bi, Luo Luo, John C. S. Lui
2022	A*	`conf`	Quasi-Newton Methods for Saddle Point Problems. NeurIPS Chengchang Liu, Luo Luo
2021	J	`jnl`	Accelerated Proximal Subsampled Newton Method. IEEE Trans. Neural Networks Learn. Syst. Haishan Ye, Luo Luo, Zhihua Zhang
2021	J	`jnl`	Approximate Newton Methods. J. Mach. Learn. Res. Haishan Ye, Luo Luo, Zhihua Zhang
2021	J	`jnl`	Finding Second-Order Stationary Point for Nonconvex-Strongly-Concave Minimax Problem. CoRR Luo Luo, Cheng Chen
2021	J	`jnl`	Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization. CoRR Luo Luo, Guangzeng Xie, Tong Zhang, Zhihua Zhang
2021	J	`jnl`	Quasi-Newton Methods for Saddle Point Problems and Beyond. CoRR Chengchang Liu, Luo Luo
2021	A*	`conf`	Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. AAAI Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye
2020	A*	`conf`	Decentralized Accelerated Proximal Gradient Descent. NeurIPS Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang
2020	J	`jnl`	Efficient Projection-Free Algorithms for Saddle Point Problems. CoRR Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu
2020	A*	`conf`	Efficient Projection-free Algorithms for Saddle Point Problems. NeurIPS Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu
2020	A*	`conf`	Efficient and Robust High-Dimensional Linear Contextual Bandits. IJCAI Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu, Yijiang Lian
2020	A*	`conf`	Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. ICML Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang
2020	J	`jnl`	Multi-consensus Decentralized Accelerated Gradient Descent. CoRR Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang
2020	J	`jnl`	Nesterov's Acceleration for Approximate Newton. J. Mach. Learn. Res. Haishan Ye, Luo Luo, Zhihua Zhang
2020	J	`jnl`	Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. CoRR Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye
2020	A*	`conf`	Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. NeurIPS Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang
2020	J	`jnl`	Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. CoRR Luo Luo, Haishan Ye, Tong Zhang
2019	J	`jnl`	A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization. CoRR Guangzeng Xie, Luo Luo, Zhihua Zhang
2019	J	`jnl`	A Stochastic Proximal Point Algorithm for Saddle-Point Problems. CoRR Luo Luo, Cheng Chen, Yujun Li, Guangzeng Xie, Zhihua Zhang
2019	J	`jnl`	Fast stochastic second-order method logarithmic in condition number. Pattern Recognit. Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang
2019	J	`jnl`	Robust Frequent Directions with Application in Online Learning. J. Mach. Learn. Res. Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang
2018	A*	`conf`	Sketched Follow-The-Regularized-Leader for Online Factorization Machine. KDD Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, Jian Pei
2017	J	`jnl`	A Unifying Framework for Convergence Analysis of Approximate Newton Methods. CoRR Haishan Ye, Luo Luo, Zhihua Zhang
2017	A*	`conf`	Approximate Newton Methods and Their Local Convergence. ICML Haishan Ye, Luo Luo, Zhihua Zhang
2017	A*	`conf`	Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. AAAI Zihao Chen, Luo Luo, Zhihua Zhang
2017	J	`jnl`	Online Learning Via Regularized Frequent Directions. CoRR Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li
2016	J	`jnl`	Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. CoRR Zihao Chen, Luo Luo, Zhihua Zhang
2016	A*	`conf`	Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA. IJCAI Qiaomin Ye, Luo Luo, Zhihua Zhang
2016	A	`conf`	Quasi-Newton Hamiltonian Monte Carlo. UAI Tianfan Fu, Luo Luo, Zhihua Zhang
2016	J	`jnl`	Revisiting Sub-sampled Newton Methods. CoRR Haishan Ye, Luo Luo, Zhihua Zhang
2016	J	`jnl`	SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions. J. Mach. Learn. Res. Shusen Wang, Luo Luo, Zhihua Zhang
2016	J	`jnl`	Variance-Reduced Second-Order Methods. CoRR Luo Luo, Zihao Chen, Zhihua Zhang, Wu-Jun Li
2015	A*	`conf`	Support Matrix Machines. ICML Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li
2014	J	`jnl`	The Modified Nystrom Method: Theories, Algorithms, and Extension. CoRR Shusen Wang, Luo Luo, Zhihua Zhang
2013	—	`conf`	Robust crowdsourced learning. IEEE BigData Zhiquan Liu, Luo Luo, Wu-Jun Li
2008	A	`conf`	Will they stay or will they go? SIGCSE Joanne McGrath Cohoon, Zhen Wu, Luo Luo