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Haishan Ye
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2020 – today
- 2024
- [j12]Jun Shang, Haishan Ye, Xiangyu Chang:
Accelerated Double-Sketching Subspace Newton. Eur. J. Oper. Res. 319(2): 484-493 (2024) - [c14]Lesi Chen, Haishan Ye, Luo Luo:
An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. AISTATS 2024: 1990-1998 - [c13]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 - [c12]Hao Di, Haishan Ye, Xiangyu Chang, Guang Dai, Ivor W. Tsang:
Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods. ICML 2024 - [c11]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. ICML 2024 - [c10]Yilong Wang, Haishan Ye, Guang Dai, Ivor W. Tsang:
Can Gaussian Sketching Converge Faster on a Preconditioned Landscape? ICML 2024 - [i26]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 abs/2402.15173 (2024) - [i25]Qihao Zhou, Haishan Ye, Luo Luo:
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. CoRR abs/2405.16126 (2024) - [i24]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 abs/2405.17761 (2024) - 2023
- [j11]Haishan Ye:
Intelligent Image Processing Technology for Badminton Robot under Machine Vision of Internet of Things. Int. J. Humanoid Robotics 20(6): 2250018:1-2250018:26 (2023) - [j10]Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang:
Multi-Consensus Decentralized Accelerated Gradient Descent. J. Mach. Learn. Res. 24: 306:1-306:50 (2023) - [j9]Haishan Ye, Dachao Lin, Xiangyu Chang
, Zhihua Zhang:
Towards explicit superlinear convergence rate for SR1. Math. Program. 199(1): 1273-1303 (2023) - [j8]Haishan Ye
Accelerated Distributed Approximate Newton Method. IEEE Trans. Neural Networks Learn. Syst. 34(11): 8642-8653 (2023) - [c9]Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang:
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. NeurIPS 2023 - [i23]Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang:
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. CoRR abs/2304.07504 (2023) - [i22]Haishan Ye:
Mirror Natural Evolution Strategies. CoRR abs/2308.00469 (2023) - [i21]Jun Chen, Haishan Ye, Mengmeng Wang, Tianxin Huang, Guang Dai, Ivor W. Tsang
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold. CoRR abs/2308.10547 (2023) - [i20]Hao Di, Yi Yang, Haishan Ye, Xiangyu Chang:
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population. CoRR abs/2310.14337 (2023) - 2022
- [j7]Dachao Lin, Haishan Ye, Zhihua Zhang:
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods. J. Mach. Learn. Res. 23: 162:1-162:40 (2022) - [c8]Rui Pan, Haishan Ye, Tong Zhang:
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. ICLR 2022 - [i19]Luo Luo, Haishan Ye:
Decentralized Stochastic Variance Reduced Extragradient Method. CoRR abs/2202.00509 (2022) - [i18]Luo Luo, Haishan Ye:
An Optimal Stochastic Algorithm for Decentralized Nonconvex Finite-sum Optimization. CoRR abs/2210.13931 (2022) - [i17]Lesi Chen, Haishan Ye, Luo Luo:
A Simple and Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. CoRR abs/2212.02387 (2022) - 2021
- [j6]Haishan Ye, Luo Luo, Zhihua Zhang:
Approximate Newton Methods. J. Mach. Learn. Res. 22: 66:1-66:41 (2021) - [j5]Haishan Ye, Tong Zhang:
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. J. Mach. Learn. Res. 22: 238:1-238:27 (2021) - [j4]Haishan Ye
Accelerated Proximal Subsampled Newton Method. IEEE Trans. Neural Networks Learn. Syst. 32(10): 4374-4388 (2021) - [c7]Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye:
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. AAAI 2021: 8793-8800 - [c6]Dachao Lin, Haishan Ye, Zhihua Zhang:
Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. NeurIPS 2021: 6646-6657 - [i16]Haishan Ye, Tong Zhang:
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. CoRR abs/2102.03990 (2021) - [i15]Haishan Ye, Dachao Lin, Zhihua Zhang:
Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations. CoRR abs/2110.08572 (2021) - [i14]Rui Pan, Haishan Ye, Tong Zhang:
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. CoRR abs/2110.14109 (2021) - 2020
- [j3]Haishan Ye, Luo Luo, Zhihua Zhang:
Nesterov's Acceleration for Approximate Newton. J. Mach. Learn. Res. 21: 142:1-142:37 (2020) - [c5]Chaoyang He, Haishan Ye, Li Shen, Tong Zhang:
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. CVPR 2020: 11990-11999 - [c4]Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang:
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. NeurIPS 2020 - [c3]Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang:
Decentralized Accelerated Proximal Gradient Descent. NeurIPS 2020 - [i13]Luo Luo, Haishan Ye, Tong Zhang:
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. CoRR abs/2001.03724 (2020) - [i12]Chaoyang He, Haishan Ye, Li Shen, Tong Zhang:
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. CoRR abs/2003.12238 (2020) - [i11]Haishan Ye, Luo Luo, Ziang Zhou
Multi-consensus Decentralized Accelerated Gradient Descent. CoRR abs/2005.00797 (2020) - [i10]Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye:
Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. CoRR abs/2009.02553 (2020) - [i9]Haishan Ye, Wei Xiong, Tong Zhang:
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction. CoRR abs/2012.15010 (2020)
2010 – 2019
- 2019
- [j2]Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang:
Fast stochastic second-order method logarithmic in condition number. Pattern Recognit. 88: 629-642 (2019) - [i8]Haishan Ye, Tong Zhang:
Mirror Natural Evolution Strategies. CoRR abs/1910.11490 (2019) - [i7]Haishan Ye, Shusen Wang, Zhihua Zhang, Tong Zhang:
Fast Generalized Matrix Regression with Applications in Machine Learning. CoRR abs/1912.12008 (2019) - 2018
- [i6]Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang:
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. CoRR abs/1812.11377 (2018) - 2017
- [j1]Haishan Ye, Yujun Li, Cheng Chen, Zhihua Zhang:
Fast Fisher discriminant analysis with randomized algorithms. Pattern Recognit. 72: 82-92 (2017) - [c2]Haishan Ye, Luo Luo, Zhihua Zhang:
Approximate Newton Methods and Their Local Convergence. ICML 2017: 3931-3939 - [i5]Haishan Ye, Luo Luo, Zhihua Zhang:
A Unifying Framework for Convergence Analysis of Approximate Newton Methods. CoRR abs/1702.08124 (2017) - [i4]Haishan Ye, Zhihua Zhang:
Nesterov's Acceleration For Approximate Newton. CoRR abs/1710.08496 (2017) - 2016
- [c1]Yujun Li, Kaichun Mo, Haishan Ye:
Accelerating Random Kaczmarz Algorithm Based on Clustering Information. AAAI 2016: 1823-1829 - [i3]Haishan Ye, Luo Luo, Zhihua Zhang:
Revisiting Sub-sampled Newton Methods. CoRR abs/1608.02875 (2016) - [i2]Haishan Ye, Qiaoming Ye, Zhihua Zhang:
Tighter bound of Sketched Generalized Matrix Approximation. CoRR abs/1609.02258 (2016) - 2015
- [i1]Yujun Li, Kaichun Mo, Haishan Ye:
Accelerating Random Kaczmarz Algorithm Based on Clustering Information. CoRR abs/1511.05362 (2015)
Coauthor Index
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