Accelerating Random Kaczmarz Algorithm Based on Clustering Information. - Yujun Li; Kaichun Mo; Haishan Ye (AAAI, 2016)

Revisiting Sub-sampled Newton Methods. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (CoRR, 2016)

Tighter bound of Sketched Generalized Matrix Approximation. - Haishan Ye; Qiaoming Ye; Zhihua Zhang 004 (CoRR, 2016)

Approximate Newton Methods and Their Local Convergence. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (ICML, 2017)

A Unifying Framework for Convergence Analysis of Approximate Newton Methods. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (CoRR, 2017)

Nesterov's Acceleration For Approximate Newton. - Haishan Ye; Zhihua Zhang 004 (CoRR, 2017)

Fast Fisher discriminant analysis with randomized algorithms. - Haishan Ye; Yujun Li; Cheng Chen 015; Zhihua Zhang 004 (Pattern Recognit., 2017)

Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. - Haishan Ye; Zhichao Huang 002; Cong Fang 001; Chris Junchi Li; Tong Zhang 001 (CoRR, 2018)

Mirror Natural Evolution Strategies. - Haishan Ye; Tong Zhang 001 (CoRR, 2019)

Fast Generalized Matrix Regression with Applications in Machine Learning. - Haishan Ye; Shusen Wang; Zhihua Zhang 004; Tong Zhang 001 (CoRR, 2019)

Fast stochastic second-order method logarithmic in condition number. - Haishan Ye; Guangzeng Xie; Luo Luo; Zhihua Zhang 004 (Pattern Recognit., 2019)

MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. - Chaoyang He 001; Haishan Ye; Li Shen 005; Tong Zhang 001 (CVPR, 2020)

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. - Luo Luo; Haishan Ye; Zhichao Huang 002; Tong Zhang 001 (NeurIPS, 2020)

Decentralized Accelerated Proximal Gradient Descent. - Haishan Ye; Ziang Zhou; Luo Luo; Tong Zhang 001 (NeurIPS, 2020)

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. - Luo Luo; Haishan Ye; Tong Zhang 001 (CoRR, 2020)

PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction. - Haishan Ye; Wei Xiong 015; Tong Zhang 001 (CoRR, 2020)

Nesterov's Acceleration for Approximate Newton. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (J. Mach. Learn. Res., 2020)

Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices. - Luo Luo; Cheng Chen; Guangzeng Xie; Haishan Ye (AAAI, 2021)

Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence. - Dachao Lin; Haishan Ye; Zhihua Zhang 004 (NeurIPS, 2021)

Greedy and Random Broyden's Methods with Explicit Superlinear Convergence Rates in Nonlinear Equations. - Haishan Ye; Dachao Lin; Zhihua Zhang 004 (CoRR, 2021)

Approximate Newton Methods. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (J. Mach. Learn. Res., 2021)

DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. - Haishan Ye; Tong Zhang 001 (J. Mach. Learn. Res., 2021)

Accelerated Proximal Subsampled Newton Method. - Haishan Ye; Luo Luo; Zhihua Zhang 004 (IEEE Trans. Neural Networks Learn. Syst., 2021)

Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. - Rui Pan 002; Haishan Ye; Tong Zhang 001 (ICLR, 2022)

Decentralized Stochastic Variance Reduced Extragradient Method. - Luo Luo; Haishan Ye (CoRR, 2022)

An Optimal Stochastic Algorithm for Decentralized Nonconvex Finite-sum Optimization. - Luo Luo; Haishan Ye (CoRR, 2022)

A Simple and Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. - Lesi Chen; Haishan Ye; Luo Luo (CoRR, 2022)

Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods. - Dachao Lin; Haishan Ye; Zhihua Zhang 004 (J. Mach. Learn. Res., 2022)

Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis. - Dachao Lin; Yuze Han; Haishan Ye; Zhihua Zhang 004 (NeurIPS, 2023)

Mirror Natural Evolution Strategies. - Haishan Ye (CoRR, 2023)

PPFL: A Personalized Federated Learning Framework for Heterogeneous Population. - Hao Di; Yi Yang; Haishan Ye; Xiangyu Chang (CoRR, 2023)

Multi-Consensus Decentralized Accelerated Gradient Descent. - Haishan Ye; Luo Luo; Ziang Zhou; Tong Zhang 001 (J. Mach. Learn. Res., 2023)

Towards explicit superlinear convergence rate for SR1. - Haishan Ye; Dachao Lin; Xiangyu Chang; Zhihua Zhang 004 (Math. Program., 2023)

Accelerated Distributed Approximate Newton Method. - Haishan Ye; Chaoyang He 001; Xiangyu Chang (IEEE Trans. Neural Networks Learn. Syst., 2023)

Intelligent Image Processing Technology for Badminton Robot under Machine Vision of Internet of Things. - Haishan Ye (Int. J. Humanoid Robotics, 2023)

An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization. - Lesi Chen; Haishan Ye; Luo Luo (AISTATS, 2024)

Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold. - Jun Chen 023; Haishan Ye; Mengmeng Wang 005; Tianxin Huang; Guang Dai; Ivor W. Tsang; Yong Liu 007 (ICLR, 2024)

Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods. - Hao Di; Haishan Ye; Xiangyu Chang; Guang Dai; Ivor W. Tsang (ICML, 2024)

Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient. - Hao Di; Haishan Ye; Yueling Zhang; Xiangyu Chang; Guang Dai; Ivor W. Tsang (ICML, 2024)

Can Gaussian Sketching Converge Faster on a Preconditioned Landscape? - Yilong Wang; Haishan Ye; Guang Dai; Ivor W. Tsang (ICML, 2024)

Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity. - Qihao Zhou; Haishan Ye; Luo Luo (NeurIPS, 2024)

Accelerated Double-Sketching Subspace Newton. - Jun Shang; Haishan Ye; Xiangyu Chang (Eur. J. Oper. Res., 2024)

DINE: Decentralized Inexact Newton With Exact Linear Convergence Rate. - Haishan Ye; Shiyuan He; Xiangyu Chang (IEEE Trans. Signal Process., 2024)

ProAdvPrompter: A Two-Stage Journey to Effective Adversarial Prompting for LLMs. - Hao Di; Tong He; Haishan Ye; Yinghui Huang 001; Xiangyu Chang; Guang Dai; Ivor W. Tsang (ICLR, 2025)

Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer. - Yanjun Zhao; Sizhe Dang; Haishan Ye; Guang Dai; Yi Qian 004; Ivor W. Tsang (ICLR, 2025)

An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization. - Haishan Ye; Yinghui Huang 001; Hao Di; Xiangyu Chang (CoRR, 2025)

Breaking the Prompt Wall (I): A Real-World Case Study of Attacking ChatGPT via Lightweight Prompt Injection. - Xiangyu Chang; Guang Dai; Hao Di; Haishan Ye (CoRR, 2025)

Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization. - Chengli Tan; Yubo Zhou; Haishan Ye; Guang Dai; Junmin Liu; Zengjie Song; Jiangshe Zhang 001; Zixiang Zhao; Yunda Hao; Yong Xu (CoRR, 2025)

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack. - Jing Xue; Zhishen Sun; Haishan Ye; Luo Luo; Xiangyu Chang; Ivor W. Tsang; Guang Dai (CoRR, 2025)

FZOO: Fast Zeroth-Order Optimizer for Fine-Tuning Large Language Models towards Adam-Scale Speed. - Sizhe Dang; Yangyang Guo; Yanjun Zhao; Haishan Ye; Xiaodong Zheng; Guang Dai; Ivor W. Tsang (CoRR, 2025)

Stochastic Diagonal Estimation Based on Matrix Quadratic Form Oracles. - Haishan Ye; Xiangyu Chang (CoRR, 2025)

Hessian-Aware Zeroth-Order Optimization. - Haishan Ye; Zhichao Huang 002; Cong Fang 001; Chris Junchi Li; Tong Zhang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2025)