Haishan Ye

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 47

Top Topics

Intelligent Image Processing

Gradient Method

Randomized Algorithm

Top Venues

J. Mach. Learn. Res.

Pattern Recognit.

Publications

Hao Di, Haishan Ye, Yueling Zhang, Xiangyu Chang, Guang Dai, Ivor W. Tsang
Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient. CoRR (2024)
Yanjun Zhao, Sizhe Dang, Haishan Ye, Guang Dai, Yi Qian, Ivor W. Tsang
Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer. CoRR (2024)
Lesi Chen, Haishan Ye, Luo Luo
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. AISTATS (2024)
Qihao Zhou, Haishan Ye, Luo Luo
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. CoRR (2024)
Jun Chen, Haishan Ye, Mengmeng Wang, Tianxin Huang, Guang Dai, Ivor W. Tsang, Yong Liu
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold. ICLR (2024)
Haishan Ye, Chaoyang He, Xiangyu Chang
Accelerated Distributed Approximate Newton Method. IEEE Trans. Neural Networks Learn. Syst. 34 (11) (2023)
Haishan Ye
Mirror Natural Evolution Strategies. CoRR (2023)
Jun Chen, Haishan Ye, Mengmeng Wang, Tianxin Huang, Guang Dai, Ivor W. Tsang, Yong Liu
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold. CoRR (2023)
Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. CoRR (2023)
Haishan Ye, Dachao Lin, Xiangyu Chang, Zhihua Zhang
Towards explicit superlinear convergence rate for SR1. Math. Program. 199 (1) (2023)
Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. NeurIPS (2023)
Haishan Ye
Intelligent Image Processing Technology for Badminton Robot under Machine Vision of Internet of Things. Int. J. Humanoid Robotics 20 (6) (2023)
Hao Di, Yi Yang, Haishan Ye, Xiangyu Chang
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population. CoRR (2023)
Luo Luo, Haishan Ye
Decentralized Stochastic Variance Reduced Extragradient Method. CoRR (2022)
Dachao Lin, Haishan Ye, Zhihua Zhang
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods. J. Mach. Learn. Res. 23 (2022)
Lesi Chen, Haishan Ye, Luo Luo
A Simple and Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. CoRR (2022)
Luo Luo, Haishan Ye
An Optimal Stochastic Algorithm for Decentralized Nonconvex Finite-sum Optimization. CoRR (2022)
Rui Pan, Haishan Ye, Tong Zhang
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. ICLR (2022)
Haishan Ye, Tong Zhang
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. J. Mach. Learn. Res. 22 (2021)
Rui Pan, Haishan Ye, Tong Zhang
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. CoRR (2021)
Haishan Ye, Dachao Lin, Zhihua Zhang
Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations. CoRR (2021)
Dachao Lin, Haishan Ye, Zhihua Zhang
Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. NeurIPS (2021)
Haishan Ye, Tong Zhang
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. CoRR (2021)
Haishan Ye, Luo Luo, Zhihua Zhang
Accelerated Proximal Subsampled Newton Method. IEEE Trans. Neural Networks Learn. Syst. 32 (10) (2021)
Haishan Ye, Luo Luo, Zhihua Zhang
Approximate Newton Methods. J. Mach. Learn. Res. 22 (2021)
Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. AAAI (2021)
Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang
Multi-consensus Decentralized Accelerated Gradient Descent. CoRR (2020)
Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. NeurIPS (2020)
Luo Luo, Haishan Ye, Tong Zhang
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. CoRR (2020)
Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. CoRR (2020)
Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang
Decentralized Accelerated Proximal Gradient Descent. NeurIPS (2020)
Chaoyang He, Haishan Ye, Li Shen, Tong Zhang
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. CVPR (2020)
Chaoyang He, Haishan Ye, Li Shen, Tong Zhang
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. CoRR (2020)
Haishan Ye, Wei Xiong, Tong Zhang
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction. CoRR (2020)
Haishan Ye, Luo Luo, Zhihua Zhang
Nesterov's Acceleration for Approximate Newton. J. Mach. Learn. Res. 21 (2020)
Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang
Fast stochastic second-order method logarithmic in condition number. Pattern Recognit. 88 (2019)
Haishan Ye, Tong Zhang
Mirror Natural Evolution Strategies. CoRR (2019)
Haishan Ye, Shusen Wang, Zhihua Zhang, Tong Zhang
Fast Generalized Matrix Regression with Applications in Machine Learning. CoRR (2019)
Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. CoRR (2018)
Haishan Ye, Zhihua Zhang
Nesterov's Acceleration For Approximate Newton. CoRR (2017)
Haishan Ye, Luo Luo, Zhihua Zhang
A Unifying Framework for Convergence Analysis of Approximate Newton Methods. CoRR (2017)
Haishan Ye, Luo Luo, Zhihua Zhang
Approximate Newton Methods and Their Local Convergence. ICML (2017)
Haishan Ye, Yujun Li, Cheng Chen, Zhihua Zhang
Fast Fisher discriminant analysis with randomized algorithms. Pattern Recognit. 72 (2017)
Haishan Ye, Luo Luo, Zhihua Zhang
Revisiting Sub-sampled Newton Methods. CoRR (2016)
Haishan Ye, Qiaoming Ye, Zhihua Zhang
Tighter bound of Sketched Generalized Matrix Approximation. CoRR (2016)
Yujun Li, Kaichun Mo, Haishan Ye
Accelerating Random Kaczmarz Algorithm Based on Clustering Information. AAAI (2016)
Yujun Li, Kaichun Mo, Haishan Ye
Accelerating Random Kaczmarz Algorithm Based on Clustering Information. CoRR (2015)