Luo Luo

Teaching

• Multivariate Statistical Analysis (2022, 2023, 2024, 2025)

• Optimization Theory (2023, 2024, 2025)

• Multivariate Statistics (2022)

Preprints

• Chengchang Liu, Luo Luo.
  Randomized Quasi-Gauss−Newton Methods for Solving General Nonlinear Equations.
  arXiv preprint:2608.27084, 2026.
  [pdf]

• Jinyang Shi, Luo Luo.
  Nonsmooth Nonconvex-Concave Minimax Optimization: Convergence Criteria and Algorithms.
  arXiv preprint:2604.21371, 2026.
  [pdf]

• Yidong Wu, Luo Luo.
  Optimal Asynchronous Stochastic Nonconvex Optimization under Heavy-Tailed Noise.
  arXiv preprint:2601.19379, 2026.
  [pdf]

• Menglian Wang, Zhuanghua Liu, Luo Luo.
  Near-Optimal Decentralized Stochastic Nonconvex Optimization with Heavy-Tailed Noise.
  arXiv preprint:2601.11435, 2026.
  [pdf]

• Jianjiang Yu, Weiguo Gao, Luo Luo.
  Explicit Global Convergence Rates of BFGS without Line Search.
  arXiv preprint:2509.22508, 2025.
  [pdf]

• Luo Luo, Yunyan Bai, Lesi Chen, Yuxing Liu, Haishan Ye.
  On the Complexity of Decentralized Smooth Nonconvex Finite-Sum Optimization.
  arXiv preprint:2210.13931, 2022.
  [pdf]

Publications

• Zhuanghua Liu, Luo Luo.
  Stochastic Bilevel Optimization with Heavy-Tailed Noise.
  Journal of Machine Learning Research (JMLR), 27(233):1−62, 2026.
  [pdf]

• Chengchang Liu, Cheng Chen, Luo Luo.
  Symmetric Rank-k Methods.
  Journal of Machine Learning Research (JMLR), 27(195):1−33, 2026.
  [pdf]

• Chengchang Liu, John C.S. Lui, Luo Luo.
  Universal Tensor Methods for Monotone Variational Inequalities.
  Journal of Optimization Theory and Applications, 211(45):1−24, 2026.
  [pdf]

• Zhiling Zhou, Zhuanghua Liu, Chengchang Liu, Luo Luo.
  Incremental Gauss−Newton Methods with Superlinear Convergence Rates.
  Journal of Optimization Theory and Applications, 209(79):1−35, 2026.
  [pdf]

• Shuyan Ge, Rujun Jiang, Luo Luo.
  Accelerating First-Order Methods for Bilevel Optimization under General Smoothness.
  Journal of Machine Learning, 5(3):391–428, 2026.
  

• Luo Luo, Xue Cui, Tingkai Jia, Cheng Chen.
  Decentralized Stochastic Nonconvex Optimization under the \((L_0, L_1)\)-Smoothness.
  In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 3470–3479, 2026.
  [pdf]

• Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang.
  Faster Newton Methods for Convex and Nonconvex Optimization in Gradient Complexity.
  In Conference on Learning Theory (COLT), pages 1088–1112, 2026.

• Hongxu Chen, Ke Wei, Xiaoming Yuan, Luo Luo.
  Stability and Generalization of Nonconvex Optimization with Heavy-Tailed Noise.
  In International Conference on Machine Learning (ICML), pages 15194–15210, 2026.
  [pdf]

• Hongxu Chen, Ke Wei, Luo Luo.
  Decentralized Non-Convex Stochastic Optimization with Heterogeneous Variance.
  In AAAI Conference on Artificial Intelligence (AAAI), 20067−20075, 2026.
  [pdf]

• Jing Xue, Zhishen Sun, Haishan Ye, Luo Luo, Xiangyu Chang, Guang Dai.
  Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack.
  In AAAI Conference on Artificial Intelligence (AAAI), pages 35967−35975, 2026.
  [pdf]

• Hongxu Chen, Ke Wei, Haishan Ye, Luo Luo.
  A Near-Optimal Algorithm for Decentralized Convex-Concave Finite-Sum Minimax Optimization.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 121043−121093, 2025. Spotlight
  [pdf]

• Binbin Huang, Luo Luo, Yanghua Xiao, Deqing Yang, Baojian Zhou.
  Accelerated Evolving Set Processes for Local PageRank Computation.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 102601−102639, 2025.
  [pdf]

• Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang.
  Solving Convex-Concave Problems with \(\tilde{\mathcal{O}}(\epsilon^{-4/7})\) Second-Order Oracle Complexity.
  In Conference on Learning Theory (COLT), pages 952−982, 2025. Best Student Paper Award
  [pdf]

• Kunjie Ren, Luo Luo.
  A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization.
  In International Conference on Machine Learning (ICML), pages 51450−51470, 2025.
  [pdf]

• Chengchang Liu, Luo Luo, John C.S. Lui.
  An Enhanced Levenberg–Marquardt Method via Gram Reduction.
  In AAAI Conference on Artificial Intelligence (AAAI), pages 18772−18779, 2025.
  [pdf]

• Lesi Chen, Luo Luo.
  Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization.
  Journal of Machine Learning Research (JMLR), 25(387):1−44, 2024.
  [pdf]

• Qihao Zhou, Haishan Ye, Luo Luo.
  Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 28009−28050, 2024.
  [pdf]

• Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low.
  Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 45438−45461, 2024.
  [pdf]

• Shihong Ding, Long Yang, Luo Luo, Cong Fang.
  Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 4718−4764, 2024.
  [pdf]

• Zhuanghua Liu, Cheng Chen, Luo Luo, Bryan Kian Hsiang Low.
  Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization.
  In International Conference on Machine Learning (ICML), pages 30842−30872, 2024. Oral
  [pdf]

• Yunyan Bai, Yuxing Liu, Luo Luo.
  On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition.
  In International Conference on Machine Learning (ICML), pages 2392−2417, 2024. Spotlight
  [pdf]

• Yuxing Liu, Lesi Chen, Luo Luo.
  Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers.
  In International Conference on Machine Learning (ICML), pages 30807−30841, 2024.
  [pdf]

• Lesi Chen, Haishan Ye, Luo Luo.
  An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization.
  In International Conference on Artificial Intelligence and Statistics (AISTATS), pages 1990−1998, 2024.
  [pdf]

• Zhuanghua Liu, Luo Luo, Bryan Kian Hsiang Low.
  Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates.
  In AAAI Conference on Artificial Intelligence (AAAI), pages 14097−14105, 2024.
  [pdf]

• Zhenwei Lin, Jingfan Xia, Qi Deng, Luo Luo.
  Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization.
  In AAAI Conference on Artificial Intelligence (AAAI), 17477−17486, 2024. Oral
  [pdf]

• Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang.
  Multi-Consensus Decentralized Accelerated Gradient Descent.
  In Journal of Machine Learning Research (JMLR), 24(306):1−50, 2023.
  [pdf]

• Haikuo Yang, Luo Luo, Chris Junchi Li, Michael I. Jordan, Maryam Fazel.
  Accelerating Inexact HyperGradient Descent for Bilevel Optimization.
  In Workshop on Optimization for Machine Learning, 2023.
  [pdf]

• Chengchang Liu, Cheng Chen, Luo Luo, John C.S. Lui.
  Block Broyden's Methods for Solving Nonlinear Equations.
  In Conference on Neural Information Processing Systems (NeurIPS), pages 47487−47499, 2023.
  [pdf]

• Chengchang Liu, Lesi Chen, Luo Luo, John C.S. Lui.
  Communication Efficient Distributed Newton Method with Fast Convergence Rates.
  In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 1406−1416, 2023.
  [pdf]

• Lesi Chen, Jing Xu, Luo Luo.
  Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization.
  In International Conference on Machine Learning (ICML), 5219−5233, 2023.
  [pdf]

• Chengchang Liu, Luo Luo.
  Quasi-Newton Methods for Saddle Point Problems.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 3975−3987, 2022. Spotlight
  [pdf]

• Lesi Chen, Boyuan Yao, Luo Luo.
  Faster Stochastic Algorithms for Minimax Optimization under Polyak-Łojasiewicz Condition.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 13921−13932, 2022.
  [pdf]

• Luo Luo, Yujun Li, Cheng Chen.
  Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 36667−36679, 2022.
  [pdf]

• Chengchang Liu, Shuxian Bi, Luo Luo, John C.S. Lui.
  Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality.
  In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 1031−1041, 2022. Best Paper Runner-Up Award
  [pdf]

• Haishan Ye, Luo Luo, Zhihua Zhang.
  Approximate Newton Methods.
  Journal of Machine Learning Research (JMLR), 22(66):1−41, 2021.
  [pdf]

• Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye.
  Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices.
  In AAAI Conference on Artificial Intelligence (AAAI), pages 8793−8800, 2021.
  [pdf]

• Haishan Ye, Luo Luo, Zhihua Zhang.
  Nesterov's Acceleration for Approximate Newton.
  Journal of Machine Learning Research (JMLR), 21(142):1−37, 2020.
  [pdf]

• Haishan Ye, Luo Luo, Zhihua Zhang.
  Accelerated Proximal Sub-Sampled Newton Method.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS) , 32(10):4374−4388, 2020.
  [pdf]

• Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang.
  Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 20566−20577, 2020.
  [pdf]

• Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu.
  Efficient Projection-Free Algorithms for Saddle Point Problems.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 10799−10808, 2020.
  [pdf]

• Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang.
  Decentralized Accelerated Proximal Gradient Descent.
  In Advances in Neural Information Processing Systems (NeurIPS), pages 18308−18317, 2020.
  [pdf]

• Guangzeng Xie, Luo Luo, Yijiang Lian, Zhihua Zhang.
  Lower Complexity Bounds for Finite-Sum Convex-Concave Minimax Optimization Problems.
  In International Conference on Machine Learning (ICML), pages 10504−10513, 2020.
  [pdf]

• Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu, Yijiang Lian.
  Efficient and Robust High-Dimensional Linear Contextual Bandits.
  In International Joint Conference on Artificial Intelligence (IJCAI), pages 4259−4265, 2020.
  [pdf]

• Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang.
  Robust Frequent Directions with Application in Online Learning.
  Journal of Machine Learning Research (JMLR) , 20(45):1−41, 2019.
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• Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang.
  Fast Stochastic Second-Order Method Logarithmic in Condition Number.
  Pattern Recognition , 88:629−642, 2019.
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• Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, Jian Pei.
  Sketched Follow-The-Regularized-Leader for Online Factorization Machine.
  In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , pages 1900−1909, 2018.
  [pdf]

• Haishan Ye, Luo Luo, Zhihua Zhang.
  Approximate Newton Methods and Their Local Convergence.
  In International Conference on Machine Learning (ICML) , pages 3931−3939, 2017.
  [pdf]

• Zihao Chen, Luo Luo, Zhihua Zhang.
  Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features.
  In AAAI Conference on Artificial Intelligence (AAAI) , pages 1812−1818, 2017. Oral
  [pdf]

• Shusen Wang, Luo Luo, Zhihua Zhang.
  SPSD Matrix Approximation vis Column Selection: Theories, Algorithms and Extensions.
  Journal of Machine Learning Research (JMLR), 17(49):1−49, 2016.
  [pdf]

• Tianfan Fu, Luo Luo, Zhihua Zhang.
  Quasi-Newton Hamiltonian Monte Carlo.
  In Conference on Uncertainty in Artificial Intelligence (UAI) , pages 212−221, 2016.
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• Qiaomin Ye, Luo Luo, Zhihua Zhang.
  Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA.
  In International Joint Conference on Artificial Intelligence (IJCAI) , pages 2301−2307, 2016.
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• Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li.
  Support Matrix Machines.
  In International Conference on Machine Learning (ICML) , pages 938−947, 2015.
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• Zhiquan Liu, Luo Luo, Wu-Jun Li.
  Robust Crowdsourced Learning.
  In IEEE International Conference on Big Data , pages 338−343, 2013.
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