Luo Luo

Teaching

• Multivariate Statistical Analysis (2022, 2023, 2024)

• Optimization Theory (2023, 2024)

• Multivariate Statistics (2022)

Preprints

• Zhiling Zhou, Zhuanghua Liu, Chengchang Liu, Luo Luo.
  Incremental Gauss–Newton Methods with Superlinear Convergence Rates.
  arXiv preprint:2407.03195, 2024.
  [pdf]

• Chengchang Liu, Cheng Chen, Luo Luo.
  Symmetric Rank-k Methods.
  arXiv preprint:2303.16188, 2023.
  [pdf]

• Chengchang Liu, Luo Luo.
  Regularized Newton Methods for Monotone Variational Inequalities with Hölder Continuous Jacobians.
  arXiv preprint:2212.07824, 2022.
  [pdf]

• Lesi Chen, Luo Luo.
  Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization.
  arXiv preprint:2208.05925, 2022.
  [pdf]

Conference Publications

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

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

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

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

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

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

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

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

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

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

• Chengchang Liu, Cheng Chen, Luo Luo, John C.S. Lui.
  Block Broyden's Methods for Solving Nonlinear Equations.
  Advances in Neural Information Processing Systems (NeurIPS), 2023.
  [pdf]

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

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

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

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

• Luo Luo, Yujun Li, Cheng Chen.
  Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization.
  Advances in Neural Information Processing Systems (NeurIPS), 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.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022. Best Paper Runner-Up Award
  [pdf]

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

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

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

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

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

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

• Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, Jian Pei.
  Sketched Follow-The-Regularized-Leader for Online Factorization Machine.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2018.
  [pdf]

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

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

• Tianfan Fu, Luo Luo, Zhihua Zhang.
  Quasi-Newton Hamiltonian Monte Carlo.
  Conference on Uncertainty in Artificial Intelligence (UAI), 2016.
  [pdf]

• Qiaomin Ye, Luo Luo, Zhihua Zhang.
  Frequent Direction Algorithms for Approximate Matrix Multiplication with Applications in CCA.
  International Joint Conference on Artificial Intelligence (IJCAI), 2016.
  [pdf]

• Luo Luo, Yubo Xie, Zhihua Zhang, Wu-Jun Li.
  Support Matrix Machines.
  International Conference on Machine Learning (ICML), 2015.
  [pdf]

• Zhiquan Liu, Luo Luo, Wu-Jun Li.
  Robust Crowdsourced Learning.
  IEEE International Conference on Big Data, 2013.
  [pdf]

Journal Publications

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

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

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

• 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.
  [pdf]

• Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang.
  Fast Stochastic Second-Order Method Logarithmic in Condition Number.
  Pattern Recognition, 88:629-642, 2019.
  [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]