A robust hybrid method for text detection in natural scenes by learning-based partial differential equations. - Zhenyu Zhao; Cong Fang 001; Zhouchen Lin; Yi Wu (Neurocomputing, 2015)

Parallel Asynchronous Stochastic Variance Reduction for Nonconvex Optimization. - Cong Fang 001; Zhouchen Lin (AAAI, 2017)

Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers. - Cong Fang 001; Feng Cheng; Zhouchen Lin (NIPS, 2017)

Feature learning via partial differential equation with applications to face recognition. - Cong Fang 001; Zhenyu Zhao; Pan Zhou 002; Zhouchen Lin (Pattern Recognit., 2017)

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. - Cong Fang 001; Chris Junchi Li; Zhouchen Lin; Tong Zhang 001 (NeurIPS, 2018)

Accelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation. - Cong Fang 001; Yameng Huang; Zhouchen Lin (CoRR, 2018)

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)

Dictionary learning with structured noise. - Pan Zhou 002; Cong Fang 001; Zhouchen Lin; Chao Zhang 001; Edward Y. Chang (Neurocomputing, 2018)

Lifted Proximal Operator Machines. - Jia Li 002; Cong Fang 001; Zhouchen Lin (AAAI, 2019)

Complexities in Projection-Free Stochastic Non-convex Minimization. - Zebang Shen; Cong Fang 001; Peilin Zhao; Junzhou Huang; Hui Qian 001 (AISTATS, 2019)

Sharp Analysis for Nonconvex SGD Escaping from Saddle Points. - Cong Fang 001; Zhouchen Lin; Tong Zhang 001 (COLT, 2019)

Learning Compact Partial Differential Equations for Color Images with Efficiency. - Zhenyu Zhao; Chenping Hou; Bo Lin; Cong Fang 001 (ICASSP, 2019)

Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations. - Cong Fang 001; Hanze Dong; Tong Zhang 001 (CoRR, 2019)

Accelerated Optimization for Machine Learning - First-Order Algorithms - Zhouchen Lin; Huan Li 007; Cong Fang 001 (Springer, 2020)

How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. - Yihong Gu; Weizhong Zhang; Cong Fang 001; Jason D. Lee; Tong Zhang 001 (NeurIPS, 2020)

Improved Analysis of Clipping Algorithms for Non-convex Optimization. - Bohang Zhang; Jikai Jin; Cong Fang 001; Liwei Wang 001 (NeurIPS, 2020)

Accelerated First-Order Optimization Algorithms for Machine Learning. - Huan Li 007; Cong Fang 001; Zhouchen Lin (Proc. IEEE, 2020)

Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters. - Huan Li 007; Cong Fang 001; Wotao Yin; Zhouchen Lin (IEEE Trans. Signal Process., 2020)

Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. - Cong Fang 001; Jason D. Lee; Pengkun Yang; Tong Zhang 001 (COLT, 2021)

Layer-Peeled Model: Toward Understanding Well-Trained Deep Neural Networks. - Cong Fang 001; Hangfeng He 001; Qi Long; Weijie J. Su (CoRR, 2021)

Mathematical Models of Overparameterized Neural Networks. - Cong Fang 001; Hanze Dong; Tong Zhang 001 (Proc. IEEE, 2021)

Alternating Direction Method of Multipliers for Machine Learning - Zhouchen Lin; Huan Li 007; Cong Fang 001 (Springer, 2022)

Training Neural Networks by Lifted Proximal Operator Machines. - Jia Li 002; Mingqing Xiao 002; Cong Fang 001; Yue Dai 003; Chao Xu 006; Zhouchen Lin (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Convex Formulation of Overparameterized Deep Neural Networks. - Cong Fang 001; Yihong Gu; Weizhong Zhang; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2022)

On the Lower Bound of Minimizing Polyak-Łojasiewicz functions. - Pengyun Yue; Cong Fang 001; Zhouchen Lin (COLT, 2023)

Zeroth-order Optimization with Weak Dimension Dependency. - Pengyun Yue; Long Yang 004; Cong Fang 001; Zhouchen Lin (COLT, 2023)

Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee. - Yuanshi Liu; Cong Fang 001; Tong Zhang 001 (NeurIPS, 2023)

Task-Robust Pre-Training for Worst-Case Downstream Adaptation. - Jianghui Wang; Yang Chen; Xingyu Xie; Cong Fang 001; Zhouchen Lin (NeurIPS, 2023)

Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium. - Shihong Ding; Hanze Dong; Cong Fang 001; Zhouchen Lin; Tong Zhang 001 (CoRR, 2023)

Environment Invariant Linear Least Squares. - Jianqing Fan; Cong Fang 001; Yihong Gu; Tong Zhang 001 (CoRR, 2023)

Policy Representation via Diffusion Probability Model for Reinforcement Learning. - Long Yang 004; Zhixiong Huang; Fenghao Lei; Yucun Zhong; Yiming Yang; Cong Fang 001; Shiting Wen; Binbin Zhou; Zhouchen Lin (CoRR, 2023)

Task-Robust Pre-Training for Worst-Case Downstream Adaptation. - Jianghui Wang; Cheng Yang; Xingyu Xie; Cong Fang 001; Zhouchen Lin (CoRR, 2023)

CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity. - Pengyun Yue; Hanzhen Zhao; Cong Fang 001; Di He 001; Liwei Wang 001; Zhouchen Lin; Song-Chun Zhu (CoRR, 2023)

Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization. - Yuanshi Liu; Hanzhen Zhao; Yang Xu 055; Pengyun Yue; Cong Fang 001 (CoRR, 2023)

Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective. - Yang Chen; Cong Fang 001; Zhouchen Lin; Bing Liu (ICML, 2024)

End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations. - Lirui Luo; Guoxi Zhang; Hongming Xu 003; Yaodong Yang 001; Cong Fang 001; Qing Li 003 (ICML, 2024)

Quantum Algorithms and Lower Bounds for Finite-Sum Optimization. - Yexin Zhang; Chenyi Zhang 003; Cong Fang 001; Liwei Wang 001; Tongyang Li (ICML, 2024)

Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition. - Shihong Ding; Long Yang 004; Luo Luo; Cong Fang 001 (NeurIPS, 2024)

Separation and Bias of Deep Equilibrium Models on Expressivity and Learning Dynamics. - Zhoutong Wu; Yimu Zhang; Cong Fang 001; Zhouchen Lin (NeurIPS, 2024)

The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing. - Yang Xu 055; Yihong Gu; Cong Fang 001 (NeurIPS, 2024)

The Implicit Bias of Heterogeneity towards Invariance and Causality. - Yang Xu 055; Yihong Gu; Cong Fang 001 (CoRR, 2024)

INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations. - Lirui Luo; Guoxi Zhang; Hongming Xu 003; Yaodong Yang 001; Cong Fang 001; Qing Li 003 (CoRR, 2024)

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning. - Yihong Gu; Cong Fang 001; Peter Bühlmann; Jianqing Fan (CoRR, 2024)

On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization. - Jiancong Xiao; Ziniu Li; Xingyu Xie; Emily J. Getzen; Cong Fang 001; Qi Long; Weijie J. Su (CoRR, 2024)

The Optimality of (Accelerated) SGD for High-Dimensional Quadratic Optimization. - Haihan Zhang; Yuanshi Liu; Qianwen Chen; Cong Fang 001 (CoRR, 2024)

Designing Universally-Approximating Deep Neural Networks: A First-Order Optimization Approach. - Zhoutong Wu; Mingqing Xiao 002; Cong Fang 001; Zhouchen Lin (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

SEPARATE: A Simple Low-rank Projection for Gradient Compression in Modern Large-scale Model Training Process. - Hanzhen Zhao; Xingyu Xie; Cong Fang 001; Zhouchen Lin (ICLR, 2025)

Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization. - Yihong Gu; Cong Fang 001; Yang Xu 055; Zijian Guo 003; Jianqing Fan (CoRR, 2025)

Optimal Algorithms in Linear Regression under Covariate Shift: On the Importance of Precondition. - Yuanshi Liu; Haihan Zhang; Qian Chen; Cong Fang 001 (CoRR, 2025)

Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression. - Shihong Ding; Haihan Zhang; Hanzhen Zhao; Cong Fang 001 (CoRR, 2025)

Learning Curves of Stochastic Gradient Descent in Kernel Regression. - Haihan Zhang; Weicheng Lin; Yuanshi Liu; Cong Fang 001 (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)