Will they stay or will they go? - Joanne McGrath Cohoon; Zhen Wu; Luo Luo (SIGCSE, 2008)

Robust crowdsourced learning. - Zhiquan Liu; Luo Luo; Wu-Jun Li (IEEE BigData, 2013)

The Modified Nystrom Method: Theories, Algorithms, and Extension. - Shusen Wang; Luo Luo; Zhihua Zhang 004 (CoRR, 2014)

Support Matrix Machines. - Luo Luo; Yubo Xie; Zhihua Zhang 004; Wu-Jun Li (ICML, 2015)

Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA. - Qiaomin Ye; Luo Luo; Zhihua Zhang 004 (IJCAI, 2016)

Quasi-Newton Hamiltonian Monte Carlo. - Tianfan Fu; Luo Luo; Zhihua Zhang 004 (UAI, 2016)

Variance-Reduced Second-Order Methods. - Luo Luo; Zihao Chen; Zhihua Zhang 004; Wu-Jun Li (CoRR, 2016)

Revisiting Sub-sampled Newton Methods. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (CoRR, 2016)

SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions. - Shusen Wang; Luo Luo; Zhihua Zhang 004 (J. Mach. Learn. Res., 2016)

Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features. - Zihao Chen; Luo Luo; Zhihua Zhang 004 (AAAI, 2017)

Approximate Newton Methods and Their Local Convergence. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (ICML, 2017)

Online Learning Via Regularized Frequent Directions. - Luo Luo; Cheng Chen 015; Zhihua Zhang 004; Wu-Jun Li (CoRR, 2017)

A Unifying Framework for Convergence Analysis of Approximate Newton Methods. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (CoRR, 2017)

Sketched Follow-The-Regularized-Leader for Online Factorization Machine. - Luo Luo; Wenpeng Zhang 003; Zhihua Zhang 004; Wenwu Zhu 001; Tong Zhang 001; Jian Pei 001 (KDD, 2018)

A General Analysis Framework of Lower Complexity Bounds for Finite-Sum Optimization. - Guangzeng Xie; Luo Luo; Zhihua Zhang 004 (CoRR, 2019)

A Stochastic Proximal Point Algorithm for Saddle-Point Problems. - Luo Luo; Cheng Chen 015; Yujun Li; Guangzeng Xie; Zhihua Zhang 004 (CoRR, 2019)

Robust Frequent Directions with Application in Online Learning. - Luo Luo; Cheng Chen 015; Zhihua Zhang 004; Wu-Jun Li; Tong Zhang 001 (J. Mach. Learn. Res., 2019)

Fast stochastic second-order method logarithmic in condition number. - Haishan Ye; Guangzeng Xie; Luo Luo; Zhihua Zhang 004 (Pattern Recognit., 2019)

Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems. - Guangzeng Xie; Luo Luo; Yijiang Lian; Zhihua Zhang 004 (ICML, 2020)

Efficient and Robust High-Dimensional Linear Contextual Bandits. - Cheng Chen 015; Luo Luo; Weinan Zhang 001; Yong Yu 001; Yijiang Lian (IJCAI, 2020)

Efficient Projection-free Algorithms for Saddle Point Problems. - Cheng Chen 015; Luo Luo; Weinan Zhang 001; Yong Yu 001 (NeurIPS, 2020)

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. - Luo Luo; Haishan Ye; Zhichao Huang 002; Tong Zhang 001 (NeurIPS, 2020)

Decentralized Accelerated Proximal Gradient Descent. - Haishan Ye; Ziang Zhou; Luo Luo; Tong Zhang 001 (NeurIPS, 2020)

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. - Luo Luo; Haishan Ye; Tong Zhang 001 (CoRR, 2020)

Nesterov's Acceleration for Approximate Newton. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (J. Mach. Learn. Res., 2020)

Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. - Luo Luo; Cheng Chen; Guangzeng Xie; Haishan Ye (AAAI, 2021)

Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization. - Luo Luo; Guangzeng Xie; Tong Zhang 001; Zhihua Zhang 004 (CoRR, 2021)

Finding Second-Order Stationary Point for Nonconvex-Strongly-Concave Minimax Problem. - Luo Luo; Cheng Chen (CoRR, 2021)

Quasi-Newton Methods for Saddle Point Problems and Beyond. - Chengchang Liu; Luo Luo (CoRR, 2021)

Approximate Newton Methods. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (J. Mach. Learn. Res., 2021)

Accelerated Proximal Subsampled Newton Method. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (IEEE Trans. Neural Networks Learn. Syst., 2021)

Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality. - Chengchang Liu; Shuxian Bi; Luo Luo; John C. S. Lui (KDD, 2022)

Faster Stochastic Algorithms for Minimax Optimization under Polyak-{ L}ojasiewicz Condition. - Lesi Chen; Boyuan Yao; Luo Luo (NeurIPS, 2022)

Quasi-Newton Methods for Saddle Point Problems. - Chengchang Liu; Luo Luo (NeurIPS, 2022)

Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization. - Luo Luo; Yujun Li; Cheng Chen 015 (NeurIPS, 2022)

Decentralized Stochastic Variance Reduced Extragradient Method. - Luo Luo; Haishan Ye (CoRR, 2022)

Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization. - Lesi Chen; Luo Luo (CoRR, 2022)

An Optimal Stochastic Algorithm for Decentralized Nonconvex Finite-sum Optimization. - Luo Luo; Haishan Ye (CoRR, 2022)

A Simple and Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. - Lesi Chen; Haishan Ye; Luo Luo (CoRR, 2022)

Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization. - Lesi Chen; Jing Xu 027; Luo Luo (ICML, 2023)

Communication Efficient Distributed Newton Method with Fast Convergence Rates. - Chengchang Liu; Lesi Chen; Luo Luo; John C. S. Lui (KDD, 2023)

Block Broyden's Methods for Solving Nonlinear Equations. - Chengchang Liu; Cheng Chen 015; Luo Luo; John C. S. Lui (NeurIPS, 2023)

Accelerating Inexact HyperGradient Descent for Bilevel Optimization. - Haikuo Yang; Luo Luo; Chris Junchi Li; Michael I. Jordan (CoRR, 2023)

Faster Stochastic Algorithms for Minimax Optimization under Polyak-Łojasiewicz Conditions. - Lesi Chen; Boyuan Yao; Luo Luo (CoRR, 2023)

Multi-Consensus Decentralized Accelerated Gradient Descent. - Haishan Ye; Luo Luo; Ziang Zhou; Tong Zhang 001 (J. Mach. Learn. Res., 2023)

Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization. - Zhenwei Lin; Jingfan Xia; Qi Deng; Luo Luo (AAAI, 2024)

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates. - Zhuanghua Liu; Luo Luo; Bryan Kian Hsiang Low (AAAI, 2024)

An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. - Lesi Chen; Haishan Ye; Luo Luo (AISTATS, 2024)

On the Complexity of Finite-Sum Smooth Optimization under the Polyak-Łojasiewicz Condition. - Yunyan Bai; Yuxing Liu; Luo Luo (ICML, 2024)

Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers. - Yuxing Liu; Lesi Chen; Luo Luo (ICML, 2024)

Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization. - Zhuanghua Liu; Cheng Chen 015; Luo Luo; Bryan Kian Hsiang Low (ICML, 2024)

Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition. - Shihong Ding; Long Yang 004; Luo Luo; Cong Fang 001 (NeurIPS, 2024)

Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization. - Zhuanghua Liu; Luo Luo; Bryan Kian Hsiang Low (NeurIPS, 2024)

Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. - Qihao Zhou; Haishan Ye; Luo Luo (NeurIPS, 2024)

Incremental Gauss-Newton Methods with Superlinear Convergence Rates. - Zhiling Zhou; Zhuanghua Liu; Chengchang Liu; Luo Luo (CoRR, 2024)

An Enhanced Levenberg-Marquardt Method via Gram Reduction. - Chengchang Liu; Luo Luo; John C. S. Lui (AAAI, 2025)

Solving Convex-Concave Problems with 풪(ε-4/7) Second-Order Oracle Complexity. - Lesi Chen; Chengchang Liu; Luo Luo; Jingzhao Zhang (COLT, 2025)

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack. - Jing Xue; Zhishen Sun; Haishan Ye; Luo Luo; Xiangyu Chang; Ivor W. Tsang; Guang Dai (CoRR, 2025)

Solving Convex-Concave Problems with 𝒪(ε-4/7) Second-Order Oracle Complexity. - Lesi Chen; Chengchang Liu; Luo Luo; Jingzhao Zhang (CoRR, 2025)