个人简介：陈洪，教授，博士生导师。研究方向为机器学习、统计学习理论。湖北省优秀博士论文获得者，入选华中农业大学十大青年岗位能手、硕彦计划青年拔尖人才。主持国家自然科学基金面上项目、青年基金等5项国家级课题。在人工智能顶会NIPS、ICML发表论文5篇，在Appl. Comput. Harmon. Anal., J. Approx. Theory, IEEE TPAMI, IEEE TNNLS, IEEE TCYB, Neural Computation, Neural Networks, Bioinformatics等知名期刊发表论文30余篇。曾在University of Texas at Arlington从事博士后研究，多次受邀赴澳门大学、香港城市大学、南方科技大学等进行合作研究。欢迎有志从事机器学习理论与应用研究的同学来课题组攻读硕士、博士及从事博士后研究。课题组现有教授（博导）2名，副教授（硕导）6名，讲师（博士）2名。课题组博士招生专业： 生物信息、农业信息工程；硕士招生专业：数学、计算机、应用统计。

个人主页：https://chenhongml.github.io/

教师个人主页：https://faculty.hzau.edu.cn/chenhong1/zh_CN/index/104057/list/index.htm

联系方式：chenh@mail.hzau.edu.cn.

2025

1. Zirui Hu, Yingjie Wang, Zheng Zhang, Hong Chen, Dacheng Tao. A Statistical Approach for Controlled Training Data Detection, ICLR 2025.

2. Wen Wen, Han Li, Tieliang Gong, Hong Chen. Towards Generalization Bounds of GCNs for Adversarially Robust Node Classification, ICLR 2025.

3. Hao Wang, Biqin Song, Hao Deng, Hong Chen. Knockoffs Inference for Partially Linear Models with Automatic Structure Discovery. AAAI 2025.

4. Jun Chen, Hong Chen, Bin Gu, Guodong Liu, Yingjie Wang, Weifu Li. Error Analysis Affected by Heavy-Tailed Gradients for Non-Convex Pairwise Stochastic Gradient Descent. AAAI 2025.

   2024
   

1. Jun Chen, Hong Chen, Bin Gu. How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization? NeurIPS 2024.

2. Ke Zhang, Ganyu Wang, Han Li, Yulong Wang, Hong Chen, Bin Gu. Asynchronous Vertical Federated Learning for Kernelized AUC Maximization. KDD 2024.

3. Yuxin Dong, Tieliang Gong, Hong Chen, Zhongjiang He, Mengxiang Li, Shuangyong Song, Chen Li. Towards Generalization beyond Pointwise Learning: A Unified Information-theoretic Perspective. ICML 2024.

4. Xuelin Zhang, Hong Chen, Bin Gu, Tieliang Gong, Feng Zheng. Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization, IJCAI 2024.

5. Wen Wen, Han Li, Tieliang Gong, Hong Chen. Towards Sharper Generalization Bounds for Adversarial Contrastive Learning, IJCAI 2024.

6. Yuxin Dong, Tieliang Gong, Hong Chen, Shujian Yu, Chen Li. Rethinking Information-theoretic Generalization: Loss Entropy Induced PAC Bounds, ICLR 2024.

7. Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han. Negative Label Guided OOD Detection with Pretrained Vision-Language Models, ICLR 2024 (spotlight).

8. Xinyue Liu, Hualin Zhang, Bin Gu, Hong Chen. General Stability Analysis for Zeroth-Order Optimization Algorithms, ICLR 2024.

9. Jiahuan Wang, Hong Chen. Towards Stability and Generalization Bounds in Decentralized Minibatch Stochastic Gradient Descent, AAAI 2024.

   2023

Jun Chen, Hong Chen, Xue Jiang, Bin Gu, Weifu Li, Tieliang Gong, Feng Zheng, On the Stability and Generalization of Triplet Learning, AAAI, 2023.

Jiahuan Wang, Jun Chen, Hong Chen, Bin Gu, Weifu Li, Xin Tang, Stability-based Generalization Analysis for Pointwise and Pairwise Learning, AAAI, 2023.

Jingxuan Liang, Xuelin Zhang, Hong Chen, Weifu Li, Xin Tang, Stepdown SLOPE for Controlled Feature Selection, AAAI, 2023.

Yuxin Dong, Tieliang Gong, Shujian Yu, Hong Chen, Chen Li, Robust and Fast Measure of Information via Low-rank Representation, AAAI, 2023.

Yingjie Wang, Xianrui Zhong, Fengxiang He, Hong Chen, Dacheng Tao, Huber Additive Models for Non-stationary Time Series Analysis, ICLR, 2022.

Xuebin Zhao, Hong Chen, Yingjie Wang, Weifu Li, Tieliang Gong, Yulong Wang, Feng Zheng. Error-based Knockoffs Inference for Controlled Feature Selection, AAAI, 2022.

Tieliang Gong, Yuxin Dong, Hong Chen, Wei Feng, Bo Dong, Chen Li. Regularized Modal Regression on Markov-dependent Observations: A Theoretical Assessment, AAAI, 2022.

Hong Chen, Yingjie Wang, Yulong Wang, Feng Zheng. Distributed ranking with communications: Approximation analysis and applications, AAAI, 2021.

Yingjie Wang, Hong Chen*, Feng Zheng, Chen Xu, Tieliang Gong.Multi-task additive models for robust estimation and automatic structure discovery, NeurIPS, 2020

Hong Chen*#, Guodong Liu#, Heng Huang. Sparse shrunk additive models, ICML, 2020. (#contributed equally)

Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang*. Group sparse additive machine, NIPS, 2017.

Xiaoqian Wang#, Hong Chen#, Dinggang Shen, Heng Huang*. Regularized modal regression with applications in cognitive impairment prediction, NIPS, 2017. (#contributed equally)

Hong Chen, Haifeng Xia, Wendong Cai, Heng Huang*. Error Analysis of Generalized Nystrom Kernel Regression, NIPS, 2016.

Hong Chen, Youcheng Fu, Xue Jiang, Yanhong Chen, Weifu Li, Yicong Zhou, Feng Zheng. Gradient Learning with the Mode-induced Loss: Consistency Analysis and Applications. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.

Yuxiang Han, Hong Chen, Tieliang Gong, Jia Cai. Robust Partially Linear Models for Automatic Structure Discovery, Expert Systems with Applications, Volume 217, 1 May 2023, 119528

Linqian Han, Wanshun Zhong, Jia Qian, Minliang Jin, Peng Tian, Wanchao Zhu, Hongwei Zhang, Yonghao Sun, Jia-Wu Feng, Xiangguo Liu, Guo Chen, Babar Farid, Ruonan Li, Zimo Xiong, Zhihui Tian, Juan Li, Zi Luo, Dengxiang Du, Sijia Chen, Qixiao Jin, Jiaxin Li, Zhao Li, Yan Liang, Xiaomeng Jin, Yong Peng, Chang Zheng, Xinnan Ye, Yuejia Yin, Hong Chen, Weifu Li, Ling-Ling Chen, Qing Li, Jianbing Yan, Fang Yang & Lin Li. A multi-omics integrative network map of maize, Nature Genetics, 2023.

Yulong Wang, Kit Ian Kou, Hong Chen, Yuan Yan Tang, Luoqing Li. Simultaneous Robust Matching Pursuit for Multi-view Learning, Pattern Recognition,Volume 134:109100, 2023.

Yulong Wang, Yap-Peng Tan, Yuan Yan Tang, Hong Chen, Cuiming Zou, Luoqing Li. Generalized and Discriminative Collaborative Representation for Multiclass Classification, IEEE Transactions on Cybernetics,52(5):2675–2686, 2022.

Tieliang Gong, Yuxin Dong, Hong Chen, Bo Dong, Chen Li. Markov Subsampling Based on Huber Criterion. IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2022.3189069, 2022.

Jun Chen, Hao Deng, Shuxin Li, Weifu Li, Hong Chen, Yanhong Chen, Bingxian Luo. RU-net: A Residual U-net for Automatic Interplanetary Coronal Mass Ejection Detection. The Astrophysical Journal Supplement Series, 259(1):8, 2022.

Hong Chen, Yingjie Wang, Feng Zheng, Cheng Deng, Heng Huang. Sparse modal additive model. IEEE Transactions on Neural Networks and Learning Systems, 32(6): 2373-2387, 2021.

Hong Chen, Changying Guo,Yingjie Wang, Huijuan Xiong. Sparse additive machine with ramp loss. Analysis and Applications, 19(3):509-528, 2021.

Yulong Wang, Yuan Yan Tang, Luoqing Li, Hong Chen. Modal regression based atomic representation for robust face recognition and reconstruction, IEEE Transactions on Cybernetics,50(10):4393–4405, 2020.

Yulong Wang,Yuan-Yan Tang, Luoqing Li, Hong Chen, Jianjia Pan. Atomic representation-based classification: theory, algorithm and applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(1): 6–19, 2019.

Xiaoqian Wang, Hong Chen, Jingwen Yan, Kwangsik Nho, Shannon L Risacher, Andrew J Saykin, Li Shen, Heng Huang. Quantitative trait loci identification for brain endophenotypes via new additive model with random networks, Bioinformatics, 34(17): i866–i874, 2018.

Hong Chen, Yulong Wang. Kernel-based sparse regression with the correntropy-induced loss, Applied and Computational Harmonic Analysis, 44(1): 144–164, 2018.

Tieliang Gong, Zongben Xu, Hong Chen. Generalization analysis of Fredholm kernel regularized classifiers, Neural Computation, 29(7): 1879–1901, 2017.

Yicong Zhou#, Hong Chen#, Rushi Lan, Zhibin Pan. Generalization performance of regularized ranking with multiscale kernels, IEEE Transactions on Neural Networks and Learning Systems, 27: 993-1002, 2016. (#contributed equally)

Hong Chen, Jiangtao Peng, Yicong Zhou, Luoqing Li, Zhibin Pan. Extreme learning machine for ranking: Generalization analysis and applications, Neural Networks, 53: 110-126, 2014.

Hong Chen, Yi Tang, Luoqing Li, Yuan Yuan, Xuelong Li,Yuan Yan Tang, Error analysis of stochastic gradient descent ranking, IEEE Transactions on Cybernetics, 43: 898–909, 2013.

Hong Chen, Zhibin Pan, Luoqing Li, Yuan Yan Tang. Error analysis of coefficient-based regularized algorithm for density-level detection, Neural Computation, 25(4): 1107–1121, 2013.

Hong Chen, Yicong Zhou, Yuan Yan Tang, Luoqing Li, Zhibin Pan, Convergence rate of semi-supervised greedy algorithm, Neural Networks, 44–50, 2013.

Hong Chen. The convergence rate of a regularized ranking algorithm, Journal of Approximation Theory, 164: 1513–1519, 2012.

Hong Chen, Luoqing Li, Jiangtao Peng. Semi-supervised learning based on high density regions estimation, Neural Networks, 23(7): 812–818, 2010.

Hong Chen, Luoqing Li. Semi-supervised multi-category classification with imperfect model, IEEE Transactions on Neural Networks, 20(10): 1594–1603, 2009.