Haishan Ye - RankMe

77 papers A* 17A 1Journal 59

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning. CoRR Zhishen Sun, Sizhe Dang, Guang Dai, Haishan Ye
2026	J	`jnl`	From O(mn) to O(r CoRR Sizhe Dang, Jiaqi Shao, Xiaodong Zheng, Guang Dai, Yan Song, Haishan Ye
2026	J	`jnl`	HALO: Hardness-aware bilevel-inspired contrastive graph clustering. Int. J. Approx. Reason. Yuchen Zhu, Kuang Zhou, Haishan Ye, Guang Dai, Ivor W. Tsang
2026	J	`jnl`	On the Convergence of Single-Loop Stochastic Bilevel Optimization with Approximate Implicit Differentiation. CoRR Yubo Zhou, Luo Luo, Guang Dai, Haishan Ye
2026	J	`jnl`	Optimal High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback. CoRR Haishan Ye
2026	J	`jnl`	PMGT-VR: A Decentralized Proximal-Gradient Algorithmic Framework With Variance Reduction. IEEE Trans. Pattern Anal. Mach. Intell. Haishan Ye, Wei Xiong, Tong Zhang
2026	A*	`conf`	Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack. AAAI Jing Xue, Zhishen Sun, Haishan Ye, Luo Luo, Xiangyu Chang, Guang Dai
2026	J	`jnl`	Warm-start or cold-start? A comparison of generalizability in gradient-based hyperparameter tuning. Neural Networks Yubo Zhou, Jun Shu, Chengli Tan, Haishan Ye, Quanziang Wang, Junmin Liu, Deyu Meng, Ivor W. Tsang, Guang Dai
2025	J	`jnl`	An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization. CoRR Haishan Ye, Yinghui Huang, Hao Di, Xiangyu Chang
2025	J	`jnl`	Breaking the Prompt Wall (I): A Real-World Case Study of Attacking ChatGPT via Lightweight Prompt Injection. CoRR Xiangyu Chang, Guang Dai, Hao Di, Haishan Ye
2025	J	`jnl`	Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithm on Stochastic Smooth Functions. CoRR Haishan Ye
2025	J	`jnl`	Explicit and Non-asymptotic Query Complexities of Rank-Based Zeroth-order Algorithms on Smooth Functions. CoRR Haishan Ye
2025	J	`jnl`	FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed. CoRR Sizhe Dang, Yangyang Guo, Yanjun Zhao, Haishan Ye, Xiaodong Zheng, Guang Dai, Ivor W. Tsang
2025	J	`jnl`	Frustratingly Easy Task-aware Pruning for Large Language Models. CoRR Yuanhe Tian, Junjie Liu, Xican Yang, Haishan Ye, Yan Song
2025	J	`jnl`	Hessian-Aware Zeroth-Order Optimization. IEEE Trans. Pattern Anal. Mach. Intell. Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang
2025	J	`jnl`	MSCR: Exploring the Vulnerability of LLMs' Mathematical Reasoning Abilities Using Multi-Source Candidate Replacement. CoRR Zhishen Sun, Guang Dai, Haishan Ye
2025	J	`jnl`	Numerical Sensitivity and Robustness: Exploring the Flaws of Mathematical Reasoning in Large Language Models. CoRR Zhishen Sun, Guang Dai, Ivor W. Tsang, Haishan Ye
2025	J	`jnl`	Optimal Decentralized Composite Optimization for Strongly Convex Functions. J. Mach. Learn. Res. Haishan Ye, Xiangyu Chang
2025	J	`jnl`	Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack. CoRR Jing Xue, Zhishen Sun, Haishan Ye, Luo Luo, Xiangyu Chang, Ivor W. Tsang, Guang Dai
2025	A*	`conf`	ProAdvPrompter: A Two-Stage Journey to Effective Adversarial Prompting for LLMs. ICLR Hao Di, Tong He, Haishan Ye, Yinghui Huang, Xiangyu Chang, Guang Dai, Ivor W. Tsang
2025	A*	`conf`	Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer. ICLR Yanjun Zhao, Sizhe Dang, Haishan Ye, Guang Dai, Yi Qian, Ivor W. Tsang
2025	J	`jnl`	Stochastic Diagonal Estimation Based on Matrix Quadratic Form Oracles. CoRR Haishan Ye, Xiangyu Chang
2025	J	`jnl`	Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization. CoRR Chengli Tan, Yubo Zhou, Haishan Ye, Guang Dai, Junmin Liu, Zengjie Song, Jiangshe Zhang, Zixiang Zhao, Yunda Hao, Yong Xu
2024	J	`jnl`	Accelerated Double-Sketching Subspace Newton. Eur. J. Oper. Res. Jun Shang, Haishan Ye, Xiangyu Chang
2024	A	`conf`	An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. AISTATS Lesi Chen, Haishan Ye, Luo Luo
2024	A*	`conf`	Can Gaussian Sketching Converge Faster on a Preconditioned Landscape? ICML Yilong Wang, Haishan Ye, Guang Dai, Ivor W. Tsang
2024	J	`jnl`	DINE: Decentralized Inexact Newton With Exact Linear Convergence Rate. IEEE Trans. Signal Process. Haishan Ye, Shiyuan He, Xiangyu Chang
2024	A*	`conf`	Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold. ICLR Jun Chen, Haishan Ye, Mengmeng Wang, Tianxin Huang, Guang Dai, Ivor W. Tsang, Yong Liu
2024	A*	`conf`	Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods. ICML Hao Di, Haishan Ye, Xiangyu Chang, Guang Dai, Ivor W. Tsang
2024	A*	`conf`	Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient. ICML Hao Di, Haishan Ye, Yueling Zhang, Xiangyu Chang, Guang Dai, Ivor W. Tsang
2024	J	`jnl`	Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient. CoRR Hao Di, Haishan Ye, Yueling Zhang, Xiangyu Chang, Guang Dai, Ivor W. Tsang
2024	A*	`conf`	Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. NeurIPS Qihao Zhou, Haishan Ye, Luo Luo
2024	J	`jnl`	Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. CoRR Qihao Zhou, Haishan Ye, Luo Luo
2024	J	`jnl`	Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer. CoRR Yanjun Zhao, Sizhe Dang, Haishan Ye, Guang Dai, Yi Qian, Ivor W. Tsang
2023	J	`jnl`	Accelerated Distributed Approximate Newton Method. IEEE Trans. Neural Networks Learn. Syst. Haishan Ye, Chaoyang He, Xiangyu Chang
2023	J	`jnl`	Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold. CoRR Jun Chen, Haishan Ye, Mengmeng Wang, Tianxin Huang, Guang Dai, Ivor W. Tsang, Yong Liu
2023	J	`jnl`	Intelligent Image Processing Technology for Badminton Robot under Machine Vision of Internet of Things. Int. J. Humanoid Robotics Haishan Ye
2023	J	`jnl`	Mirror Natural Evolution Strategies. CoRR Haishan Ye
2023	J	`jnl`	Multi-Consensus Decentralized Accelerated Gradient Descent. J. Mach. Learn. Res. Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang
2023	J	`jnl`	PPFL: A Personalized Federated Learning Framework for Heterogeneous Population. CoRR Hao Di, Yi Yang, Haishan Ye, Xiangyu Chang
2023	A*	`conf`	Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. NeurIPS Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang
2023	J	`jnl`	Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. CoRR Dachao Lin, Yuze Han, Haishan Ye, Zhihua Zhang
2023	J	`jnl`	Towards explicit superlinear convergence rate for SR1. Math. Program. Haishan Ye, Dachao Lin, Xiangyu Chang, Zhihua Zhang
2022	J	`jnl`	A Simple and Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. CoRR Lesi Chen, Haishan Ye, Luo Luo
2022	J	`jnl`	An Optimal Stochastic Algorithm for Decentralized Nonconvex Finite-sum Optimization. CoRR Luo Luo, Haishan Ye
2022	J	`jnl`	Decentralized Stochastic Variance Reduced Extragradient Method. CoRR Luo Luo, Haishan Ye
2022	A*	`conf`	Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. ICLR Rui Pan, Haishan Ye, Tong Zhang
2022	J	`jnl`	Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods. J. Mach. Learn. Res. Dachao Lin, Haishan Ye, Zhihua Zhang
2021	J	`jnl`	Accelerated Proximal Subsampled Newton Method. IEEE Trans. Neural Networks Learn. Syst. Haishan Ye, Luo Luo, Zhihua Zhang
2021	J	`jnl`	Approximate Newton Methods. J. Mach. Learn. Res. Haishan Ye, Luo Luo, Zhihua Zhang
2021	J	`jnl`	DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. CoRR Haishan Ye, Tong Zhang
2021	J	`jnl`	DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. J. Mach. Learn. Res. Haishan Ye, Tong Zhang
2021	J	`jnl`	Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. CoRR Rui Pan, Haishan Ye, Tong Zhang
2021	J	`jnl`	Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations. CoRR Haishan Ye, Dachao Lin, Zhihua Zhang
2021	A*	`conf`	Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. NeurIPS Dachao Lin, Haishan Ye, Zhihua Zhang
2021	A*	`conf`	Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. AAAI Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye
2020	A*	`conf`	Decentralized Accelerated Proximal Gradient Descent. NeurIPS Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang
2020	A*	`conf`	MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. CVPR Chaoyang He, Haishan Ye, Li Shen, Tong Zhang
2020	J	`jnl`	MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. CoRR Chaoyang He, Haishan Ye, Li Shen, Tong Zhang
2020	J	`jnl`	Multi-consensus Decentralized Accelerated Gradient Descent. CoRR Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang
2020	J	`jnl`	Nesterov's Acceleration for Approximate Newton. J. Mach. Learn. Res. Haishan Ye, Luo Luo, Zhihua Zhang
2020	J	`jnl`	PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction. CoRR Haishan Ye, Wei Xiong, Tong Zhang
2020	J	`jnl`	Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. CoRR Luo Luo, Cheng Chen, Guangzeng Xie, Haishan Ye
2020	A*	`conf`	Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. NeurIPS Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang
2020	J	`jnl`	Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. CoRR Luo Luo, Haishan Ye, Tong Zhang
2019	J	`jnl`	Fast Generalized Matrix Regression with Applications in Machine Learning. CoRR Haishan Ye, Shusen Wang, Zhihua Zhang, Tong Zhang
2019	J	`jnl`	Fast stochastic second-order method logarithmic in condition number. Pattern Recognit. Haishan Ye, Guangzeng Xie, Luo Luo, Zhihua Zhang
2019	J	`jnl`	Mirror Natural Evolution Strategies. CoRR Haishan Ye, Tong Zhang
2018	J	`jnl`	Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. CoRR Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang
2017	J	`jnl`	A Unifying Framework for Convergence Analysis of Approximate Newton Methods. CoRR Haishan Ye, Luo Luo, Zhihua Zhang
2017	A*	`conf`	Approximate Newton Methods and Their Local Convergence. ICML Haishan Ye, Luo Luo, Zhihua Zhang
2017	J	`jnl`	Fast Fisher discriminant analysis with randomized algorithms. Pattern Recognit. Haishan Ye, Yujun Li, Cheng Chen, Zhihua Zhang
2017	J	`jnl`	Nesterov's Acceleration For Approximate Newton. CoRR Haishan Ye, Zhihua Zhang
2016	A*	`conf`	Accelerating Random Kaczmarz Algorithm Based on Clustering Information. AAAI Yujun Li, Kaichun Mo, Haishan Ye
2016	J	`jnl`	Revisiting Sub-sampled Newton Methods. CoRR Haishan Ye, Luo Luo, Zhihua Zhang
2016	J	`jnl`	Tighter bound of Sketched Generalized Matrix Approximation. CoRR Haishan Ye, Qiaoming Ye, Zhihua Zhang
2015	J	`jnl`	Accelerating Random Kaczmarz Algorithm Based on Clustering Information. CoRR Yujun Li, Kaichun Mo, Haishan Ye