The Implicit Bias of Heterogeneity towards Invariance and Causality
[arXiv]
Yang Xu1, Yihong Gu1, Cong Fang,
Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization [arXiv]
Yuanshi Liu, Hanzhen Zhao, Yang Xu, Pengyun Yue, Cong Fang,
Environment Invariant Linear Least Squares [arXiv]
Jianqing fan, Cong Fang, Yihong Gu, and Tong Zhang (α-β order),
Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee [arXiv]
Yuanshi Liu, Cong Fang*, and Tong Zhang,
Advances in Neural Information Processing Systems (NeurIPS), 2023.
Task-Robust Pre-Training for Worst-Case Downstream Adaptation [arXiv]
Jianghui Wang1, Yang Chen1, Xingyu Xie, Cong Fang*, and Zhouchen Lin*,
Advances in Neural Information Processing Systems (NeurIPS), 2023.
Zeroth-order Optimization with Weak Dimension Dependency [arXiv]
Pengyun Yue, Long Yang, Cong Fang*, and Zhouchen Lin*,
Annual Conference on Learning Theory (COLT), 2023.
On the Lower Bound of Minimizing Polyak-Ćojasiewicz Functions [arXiv]
Pengyun Yue, Cong Fang*, and Zhouchen Lin*,
Annual Conference on Learning Theory (COLT), 2023.
Layer-Peeled Model: Toward Understanding Well-Trained Deep Neural Networks [arXiv]
Cong Fang, Hangfeng He, Qi Long, and Weijie Su (α-β order),
Proceedings of the National Academy of Sciences (top journal: PNAS), 2021, accepted.
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Network [arXiv]
Cong Fang, Jason D. Lee, Pengkun Yang, and Tong Zhang (α-β order),
Annual Conference on Learning Theory (COLT), 2021.
Mathematical Models of Overparameterized Neural Networks
[arXiv]
Cong Fang, Hanze Dong, and Tong Zhang,
Proceedings of the IEEE (the flagship journal of IEEE: PIEEE), 2021.
How to Characterize the Landscape of Overparameterized Convolutional Neural Networks
[paper]
Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, and Tong Zhang,
Advances in Neural Information Processing Systems (NeurIPS), 2020.
Improved Analysis of Clipping Algorithms for Non-convex Optimization
[paper][arXiv]
Bohang Zhang, Jikai Jin, Cong Fang, and Liwei Wang,
Advances in Neural Information Processing Systems (NeurIPS), 2020.
Accelerated First-Order Optimization Algorithms for Machine Learning
[paper]
Huan Li*, Cong Fang*, and Zhouchen Lin (*equal contribution),
Proceedings of the IEEE (the flagship journal of IEEE: PIEEE), 2020.
Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters
[paper][arXiv]
Huan Li, Cong Fang, Zhouchen Lin, and Wotao Lin,
IEEE Trans. on Signal Processing (top signal processing journal: TSP), 2020.
Training Deep Neural Networks by Lifted Proximal Operator Machines
[paper]
Jia Li, Mingqing Xiao, Cong Fang, Daiyue, Chao Xu, and Zhouchen Lin,
IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 2020.
Complexities in Projection-Free Stochastic Non-convex Minimization
[paper]
Zebang Shen, Cong Fang, Peilin Zhao, Junzhou Huang, and Hui Qian,
The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
Sharp Analysis for Nonconvex SGD Escaping from Saddle Points [paper][arXiv]
Cong Fang, Zhouchen Lin, and Tong Zhang (α-β order),
Annual Conference on Learning Theory (COLT), 2019.
Lifted Proximal Operator Machines
[paper][arXiv]
Jia Li, Cong Fang, and Zhouchen Lin,
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018.
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
[paper][arXiv]
Cong Fang, Chris Junchi Li, Zhouchen Lin, and Tong Zhang (α-β order),
Advances in Neural Information Processing Systems (NeurIPS), 2018.
Dictionary learning with structured noise
[paper]
Pan Zhou, Cong Fang, Zhouchen Lin, Chao Zhang, and Edward Chang,
Neurocomputing, 2018.
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers
[paper]
Cong Fang, Feng Cheng, and Zhouchen Lin,
Advances in Neural Information Processing Systems (NeurIPS), 2017.
Parallel Asynchronous Stochastic Variance Reduction for Nonconvex Optimization
[paper]
Cong Fang and Zhouchen Lin,
Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017.
Feature Learning via Partial Differential Equation with Applications to Face Recognition
[paper]
Cong Fang, Zhenyu Zhao, Pan Zhou, and Zhouchen Lin,
Pattern Recognition (PR), 2017.
A Robust Hybrid Method for Text Detection in Natural Scenes by Learning-based Partial Differential Equations
[paper]
Zhenyu Zhao, Cong Fang, Zhouchen Lin, and Yi Wu,
Neurocomputing, 2015.
Accelerated Optimization in Machine Learning: First-Order Algorithms [book]
Zhouchen Lin, Huan Li, and Cong Fang, Springer, 2020.
I am in charge of introducing stochastic and distributed algorithms (Chapters 5 and 6)