Cong Fang

According to our database, Cong Fang authored at least 39 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations.

Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang, Cong Fang, Qing Li

CoRR, 2024

The Implicit Bias of Heterogeneity towards Invariance and Causality.

Yang Xu, Yihong Gu, Cong Fang

CoRR, 2024

2023

Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization.

Yuanshi Liu, Hanzhen Zhao, Yang Xu, Pengyun Yue, Cong Fang

CoRR, 2023

CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity.

Pengyun Yue, Hanzhen Zhao, Cong Fang, Di He, Liwei Wang, Zhouchen Lin, Song-Chun Zhu

CoRR, 2023

Task-Robust Pre-Training for Worst-Case Downstream Adaptation.

Jianghui Wang, Cheng Yang, Xingyu Xie, Cong Fang, Zhouchen Lin

CoRR, 2023

Policy Representation via Diffusion Probability Model for Reinforcement Learning.

Long Yang, Zhixiong Huang, Fenghao Lei, Yucun Zhong, Yiming Yang, Cong Fang, Shiting Wen, Binbin Zhou, Zhouchen Lin

CoRR, 2023

Environment Invariant Linear Least Squares.

Jianqing Fan, Cong Fang, Yihong Gu, Tong Zhang

CoRR, 2023

Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium.

Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang

CoRR, 2023

Task-Robust Pre-Training for Worst-Case Downstream Adaptation.

Jianghui Wang, Yang Chen, Xingyu Xie, Cong Fang, Zhouchen Lin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee.

Yuanshi Liu, Cong Fang, Tong Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Zeroth-order Optimization with Weak Dimension Dependency.

Pengyun Yue, Long Yang, Cong Fang, Zhouchen Lin

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

On the Lower Bound of Minimizing Polyak-Łojasiewicz functions.

Pengyun Yue, Cong Fang, Zhouchen Lin

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

FastRoadSeg: Fast Monocular Road Segmentation Network.

Shi Gong, Huan Zhou, Feng Xue, Cong Fang, Yiqun Li, Yu Zhou

IEEE Trans. Intell. Transp. Syst., 2022

Convex Formulation of Overparameterized Deep Neural Networks.

Cong Fang, Yihong Gu, Weizhong Zhang, Tong Zhang

IEEE Trans. Inf. Theory, 2022

Training Neural Networks by Lifted Proximal Operator Machines.

Jia Li, Mingqing Xiao, Cong Fang, Yue Dai, Chao Xu, Zhouchen Lin

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Smart Electronic Nose Enabled by an All-Feature Olfactory Algorithm.

Cong Fang, Hua-Yao Li, Long Li, Hu-Yin Su, Jiang Tang, Xiang Bai, Huan Liu

Adv. Intell. Syst., 2022

Alternating Direction Method of Multipliers for Machine Learning

Zhouchen Lin, Huan Li, Cong Fang

Springer, ISBN: 978-981-16-9839-2, 2022

2021

Mathematical Models of Overparameterized Neural Networks.

Cong Fang, Hanze Dong, Tong Zhang

Proc. IEEE, 2021

Deep learning for predicting COVID-19 malignant progression.

Cong Fang, Song Bai, Qianlan Chen, Yu Zhou, Liming Xia, Lixin Qin, Shi Gong, Xudong Xie, Chunhua Zhou, Dandan Tu, Changzheng Zhang, Xiaowu Liu, Weiwei Chen, Xiang Bai, Philip H. S. Torr

Medical Image Anal., 2021

Layer-Peeled Model: Toward Understanding Well-Trained Deep Neural Networks.

Cong Fang, Hangfeng He, Qi Long, Weijie J. Su

CoRR, 2021

Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks.

Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang

Proceedings of the Conference on Learning Theory, 2021

2020

Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters.

Huan Li, Cong Fang, Wotao Yin, Zhouchen Lin

IEEE Trans. Signal Process., 2020

Accelerated First-Order Optimization Algorithms for Machine Learning.

Huan Li, Cong Fang, Zhouchen Lin

Proc. IEEE, 2020

Improved Analysis of Clipping Algorithms for Non-convex Optimization.

Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

How to Characterize The Landscape of Overparameterized Convolutional Neural Networks.

Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, Tong Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Accelerated Optimization for Machine Learning - First-Order Algorithms

Zhouchen Lin, Huan Li, Cong Fang

Springer, ISBN: 978-981-15-2909-2, 2020

2019

Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations.

Cong Fang, Hanze Dong, Tong Zhang

CoRR, 2019

Learning Compact Partial Differential Equations for Color Images with Efficiency.

Zhenyu Zhao, Chenping Hou, Bo Lin, Cong Fang

Proceedings of the IEEE International Conference on Acoustics, 2019

Sharp Analysis for Nonconvex SGD Escaping from Saddle Points.

Cong Fang, Zhouchen Lin, Tong Zhang

Proceedings of the Conference on Learning Theory, 2019

Complexities in Projection-Free Stochastic Non-convex Minimization.

Zebang Shen, Cong Fang, Peilin Zhao, Junzhou Huang, Hui Qian

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Lifted Proximal Operator Machines.

Jia Li, Cong Fang, Zhouchen Lin

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Dictionary learning with structured noise.

Pan Zhou, Cong Fang, Zhouchen Lin, Chao Zhang, Edward Y. Chang

Neurocomputing, 2018

Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack.

Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang

CoRR, 2018

Accelerating Asynchronous Algorithms for Convex Optimization by Momentum Compensation.

Cong Fang, Yameng Huang, Zhouchen Lin

CoRR, 2018

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator.

Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Feature learning via partial differential equation with applications to face recognition.

Cong Fang, Zhenyu Zhao, Pan Zhou, Zhouchen Lin

Pattern Recognit., 2017

Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers.

Cong Fang, Feng Cheng, Zhouchen Lin

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Parallel Asynchronous Stochastic Variance Reduction for Nonconvex Optimization.

Cong Fang, Zhouchen Lin

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2015

A robust hybrid method for text detection in natural scenes by learning-based partial differential equations.

Zhenyu Zhao, Cong Fang, Zhouchen Lin, Yi Wu

Neurocomputing, 2015