Cong Fang - RankMe

70 papers A* 19A 1Misc 1Journal 46Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Accelerating Single-Pass SGD for Generalized Linear Prediction. CoRR Qian Chen, Shihong Ding, Cong Fang
2026	J	`jnl`	MVR: Multi-view Video Reward Shaping for Reinforcement Learning. CoRR Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang, Cong Fang, Qing Li
2026	J	`jnl`	PRAC: Principal-Random Subspace for LLM Activation Compression and Memory-Efficient Training. CoRR Yanyi Li, Yimu Zhang, Cong Fang
2026	J	`jnl`	Partial Feedback Online Learning. CoRR Shihao Shao, Cong Fang, Zhouchen Lin, Dacheng Tao
2025	J	`jnl`	AdaPM: a Partial Momentum Algorithm for LLM Training. CoRR Yimu Zhang, Yuanshi Liu, Cong Fang
2025	J	`jnl`	Conda: Column-Normalized Adam for Training Large Language Models Faster. CoRR Junjie Wang, Pan Zhou, Yiming Dong, Huan Li, Jia Li, Xun Zhou, Qicheng Lao, Cong Fang, Zhouchen Lin
2025	J	`jnl`	Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization. CoRR Yihong Gu, Cong Fang, Yang Xu, Zijian Guo, Jianqing Fan
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`	Improving Model Representation and Reducing KV Cache via Skip Connections with First Value Heads. CoRR Zhoutong Wu, Yuan Zhang, Yiming Dong, Chenheng Zhang, Cong Fang, Kun Yuan, Zhouchen Lin
2025	A*	`conf`	Learning Curves of Stochastic Gradient Descent in Kernel Regression. ICML Haihan Zhang, Weicheng Lin, Yuanshi Liu, Cong Fang
2025	J	`jnl`	Learning Curves of Stochastic Gradient Descent in Kernel Regression. CoRR Haihan Zhang, Weicheng Lin, Yuanshi Liu, Cong Fang
2025	J	`jnl`	Optimal Algorithms in Linear Regression under Covariate Shift: On the Importance of Precondition. CoRR Yuanshi Liu, Haihan Zhang, Qian Chen, Cong Fang
2025	A*	`conf`	SEPARATE: A Simple Low-rank Projection for Gradient Compression in Modern Large-scale Model Training Process. ICLR Hanzhen Zhao, Xingyu Xie, Cong Fang, Zhouchen Lin
2025	J	`jnl`	Scaling Law for Stochastic Gradient Descent in Quadratically Parameterized Linear Regression. CoRR Shihong Ding, Haihan Zhang, Hanzhen Zhao, Cong Fang
2024	J	`jnl`	Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning. CoRR Yihong Gu, Cong Fang, Peter Bühlmann, Jianqing Fan
2024	J	`jnl`	Designing Universally-Approximating Deep Neural Networks: A First-Order Optimization Approach. IEEE Trans. Pattern Anal. Mach. Intell. Zhoutong Wu, Mingqing Xiao, Cong Fang, Zhouchen Lin
2024	A*	`conf`	End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations. ICML Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang, Cong Fang, Qing Li
2024	J	`jnl`	INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations. CoRR Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang, Cong Fang, Qing Li
2024	J	`jnl`	On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization. CoRR Jiancong Xiao, Ziniu Li, Xingyu Xie, Emily J. Getzen, Cong Fang, Qi Long, Weijie J. Su
2024	A*	`conf`	Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition. NeurIPS Shihong Ding, Long Yang, Luo Luo, Cong Fang
2024	J	`jnl`	PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium. J. Mach. Learn. Res. Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang
2024	A*	`conf`	Quantum Algorithms and Lower Bounds for Finite-Sum Optimization. ICML Yexin Zhang, Chenyi Zhang, Cong Fang, Liwei Wang, Tongyang Li
2024	J	`jnl`	Quantum Algorithms and Lower Bounds for Finite-Sum Optimization. CoRR Yexin Zhang, Chenyi Zhang, Cong Fang, Liwei Wang, Tongyang Li
2024	A*	`conf`	Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective. ICML Yang Chen, Cong Fang, Zhouchen Lin, Bing Liu
2024	J	`jnl`	Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective. CoRR Yang Chen, Cong Fang, Zhouchen Lin, Bing Liu
2024	A*	`conf`	Separation and Bias of Deep Equilibrium Models on Expressivity and Learning Dynamics. NeurIPS Zhoutong Wu, Yimu Zhang, Cong Fang, Zhouchen Lin
2024	J	`jnl`	The Implicit Bias of Heterogeneity towards Invariance and Causality. CoRR Yang Xu, Yihong Gu, Cong Fang
2024	A*	`conf`	The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing. NeurIPS Yang Xu, Yihong Gu, Cong Fang
2024	J	`jnl`	The Optimality of (Accelerated) SGD for High-Dimensional Quadratic Optimization. CoRR Haihan Zhang, Yuanshi Liu, Qianwen Chen, Cong Fang
2023	J	`jnl`	Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization. CoRR Yuanshi Liu, Hanzhen Zhao, Yang Xu, Pengyun Yue, Cong Fang
2023	J	`jnl`	CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity. CoRR Pengyun Yue, Hanzhen Zhao, Cong Fang, Di He, Liwei Wang, Zhouchen Lin, Song-Chun Zhu
2023	A*	`conf`	Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee. NeurIPS Yuanshi Liu, Cong Fang, Tong Zhang
2023	J	`jnl`	Environment Invariant Linear Least Squares. CoRR Jianqing Fan, Cong Fang, Yihong Gu, Tong Zhang
2023	A*	`conf`	On the Lower Bound of Minimizing Polyak-Łojasiewicz functions. COLT Pengyun Yue, Cong Fang, Zhouchen Lin
2023	J	`jnl`	Policy Representation via Diffusion Probability Model for Reinforcement Learning. CoRR Long Yang, Zhixiong Huang, Fenghao Lei, Yucun Zhong, Yiming Yang, Cong Fang, Shiting Wen, Binbin Zhou, Zhouchen Lin
2023	J	`jnl`	Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium. CoRR Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang
2023	A*	`conf`	Task-Robust Pre-Training for Worst-Case Downstream Adaptation. NeurIPS Jianghui Wang, Yang Chen, Xingyu Xie, Cong Fang, Zhouchen Lin
2023	J	`jnl`	Task-Robust Pre-Training for Worst-Case Downstream Adaptation. CoRR Jianghui Wang, Cheng Yang, Xingyu Xie, Cong Fang, Zhouchen Lin
2023	A*	`conf`	Zeroth-order Optimization with Weak Dimension Dependency. COLT Pengyun Yue, Long Yang, Cong Fang, Zhouchen Lin
2022	—	`book`	Alternating Direction Method of Multipliers for Machine Learning Zhouchen Lin, Huan Li, Cong Fang
2022	J	`jnl`	Convex Formulation of Overparameterized Deep Neural Networks. IEEE Trans. Inf. Theory Cong Fang, Yihong Gu, Weizhong Zhang, Tong Zhang
2022	J	`jnl`	Training Neural Networks by Lifted Proximal Operator Machines. IEEE Trans. Pattern Anal. Mach. Intell. Jia Li, Mingqing Xiao, Cong Fang, Yue Dai, Chao Xu, Zhouchen Lin
2021	J	`jnl`	Layer-Peeled Model: Toward Understanding Well-Trained Deep Neural Networks. CoRR Cong Fang, Hangfeng He, Qi Long, Weijie J. Su
2021	J	`jnl`	Mathematical Models of Overparameterized Neural Networks. Proc. IEEE Cong Fang, Hanze Dong, Tong Zhang
2021	A*	`conf`	Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. COLT Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang
2020	J	`jnl`	Accelerated First-Order Optimization Algorithms for Machine Learning. Proc. IEEE Huan Li, Cong Fang, Zhouchen Lin
2020	—	`book`	Accelerated Optimization for Machine Learning - First-Order Algorithms Zhouchen Lin, Huan Li, Cong Fang
2020	J	`jnl`	Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters. IEEE Trans. Signal Process. Huan Li, Cong Fang, Wotao Yin, Zhouchen Lin
2020	A*	`conf`	How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. NeurIPS Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, Tong Zhang
2020	A*	`conf`	Improved Analysis of Clipping Algorithms for Non-convex Optimization. NeurIPS Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang
2020	J	`jnl`	Improved Analysis of Clipping Algorithms for Non-convex Optimization. CoRR Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang
2020	J	`jnl`	Mathematical Models of Overparameterized Neural Networks. CoRR Cong Fang, Hanze Dong, Tong Zhang
2020	J	`jnl`	Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. CoRR Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang
2019	A	`conf`	Complexities in Projection-Free Stochastic Non-convex Minimization. AISTATS Zebang Shen, Cong Fang, Peilin Zhao, Junzhou Huang, Hui Qian
2019	J	`jnl`	Convex Formulation of Overparameterized Deep Neural Networks. CoRR Cong Fang, Yihong Gu, Weizhong Zhang, Tong Zhang
2019	Misc	`conf`	Learning Compact Partial Differential Equations for Color Images with Efficiency. ICASSP Zhenyu Zhao, Chenping Hou, Bo Lin, Cong Fang
2019	A*	`conf`	Lifted Proximal Operator Machines. AAAI Jia Li, Cong Fang, Zhouchen Lin
2019	J	`jnl`	Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations. CoRR Cong Fang, Hanze Dong, Tong Zhang
2019	A*	`conf`	Sharp Analysis for Nonconvex SGD Escaping from Saddle Points. COLT Cong Fang, Zhouchen Lin, Tong Zhang
2019	J	`jnl`	Sharp Analysis for Nonconvex SGD Escaping from Saddle Points. CoRR Cong Fang, Zhouchen Lin, Tong Zhang
2018	J	`jnl`	Accelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation. CoRR Cong Fang, Yameng Huang, Zhouchen Lin
2018	J	`jnl`	Dictionary learning with structured noise. Neurocomputing Pan Zhou, Cong Fang, Zhouchen Lin, Chao Zhang, Edward Y. Chang
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
2018	J	`jnl`	Lifted Proximal Operator Machines. CoRR Jia Li, Cong Fang, Zhouchen Lin
2018	J	`jnl`	SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator. CoRR Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
2018	A*	`conf`	SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. NeurIPS Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
2017	—	`conf`	Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers. NIPS Cong Fang, Feng Cheng, Zhouchen Lin
2017	J	`jnl`	Feature learning via partial differential equation with applications to face recognition. Pattern Recognit. Cong Fang, Zhenyu Zhao, Pan Zhou, Zhouchen Lin
2017	A*	`conf`	Parallel Asynchronous Stochastic Variance Reduction for Nonconvex Optimization. AAAI Cong Fang, Zhouchen Lin
2015	J	`jnl`	A robust hybrid method for text detection in natural scenes by learning-based partial differential equations. Neurocomputing Zhenyu Zhao, Cong Fang, Zhouchen Lin, Yi Wu