dr. H. Hang (Hanyuan)

About Me

I am a machine learning researcher. My current core research area lies in ensemble learning algorithms including boosting, bagging and random forest. I use ideas from statistical learning theory to analyze and develop machine learning algorithms for problems such as regression, classification, density estimation, clustering, and anomaly detection. 


  • Statistical Learning Theory
  • Kernel-Based Learning
  • Ensemble Learning 
  • Generative Learning
  • Class-Imbalance Learning
  • Cost-Sensitive Learning
  • Weakly-Supervised Learning
  • Semi-Supervised Learning
  • Active Learning
  • Cluster Analysis
  • Transfer Learning
  • Robust Learning



H. Wen, A. Betken, and H. Hang, Class Probability Matching Using Kernel Methods for Label Shift Adaptationtechnical report, 2023. [preprint]

Y. Cai, Y. Ma, H. Yang, H. Hang, Bagged Regularized k-Distances for Anomaly Detectiontechnical report, 2023. [preprint]

H. Hang, Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Setstechnical report, 2022. [preprint]

J. Liu, H. Hang, S. Ma, X. Shen, H. Wang, and Y. Shi, Self-supervised Random Forest on Transformed Distribution for Anomaly Detection, technical report, 2022. 

H. Hang, Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learningtechnical report, 2021. [preprint]  


Peer-Reviewed Articles

J. Liu, H. Hang, B. Wang, B. Li, H. Wang, Y. Tian, and Y. Shi, GAN-CL: Generative Adversarial Networks for Learning from Complementary Labels, IEEE Transactions on Cybernetics, 53(1): 236-247, 2023. [final

H. Wen and H. Hang, Random Forest Density Estimation, In Proceedings of the 39th International Conference on Machine Learning, PMLR 162:23701-23722, 2022. [final]

H. Hang, Y. Cai, H. Yang, and Z. Lin, Under-bagging Nearest Neighbors for Imbalanced ClassificationJournal of Machine Learning Research, 23(118):1-63, 2022. [final]

J. Liu, B. Wang, H. Hang, H. Wang, Z. Qi, Y. Tian, and Y. Shi, LLP-GAN: A GAN based Algorithm for Learning from Label Proportions, IEEE Transactions on Neural Networks and Learning Systems, 34(11):8377-8388, 2022. [final]

H. Wen, J. Cui, H. Hang, J. Liu, Y. Wang, and Z. Lin, Leveraged Weighted Loss for Partial Label Learning, In Proceedings of the 38th International Conference on Machine Learning, PMLR 139:11091-11100, 2021 (Long Talk, Acceptance Rate 3%). [final]

J. Cui, H. Hang, Y. Wang, and Z. Lin, GBHT: Gradient Boosting Histogram Transform for Density Estimation, In Proceedings of the 38th International Conference on Machine Learning, PMLR 139:2233-2243, 2021. [final]

H. Hang and I. Steinwart, Optimal learning with anisotropic Gaussian SVMs, Applied and Computational Harmonic Analysis, 55:337-367, 2021. [final]

H. Hang, Z. Lin, X. Liu, and H. Wen, Histogram Transform Ensembles for Large-scale Regression, Journal of Machine Learning Research, 22(95):1-87, 2021. [final]

Y. Cai, H. Hang, H. Yang, and Z. Lin, Boosted Histogram Transform for Regression, In Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1251-1261, 2020. [final]

C. Zhang, X. Gao, M. Hsieh, H. Hang, and D. Tao, Matrix Infinitely Divisible Series: Their Inequalities and Their Applications, IEEE Transactions on Information Theory, 66(2):1099-1117, 2020. [final]

Z. Tan, Y. Yang, J. Wan, H. Hang, G. Guo, and S.Z. Li, Attention-Based Pedestrian Attribute Analysis, IEEE Transactions on Image Processing, 28(12):6126-6140, 2019. [final

H. Hang, I. Steinwart, Y. Feng, and J.A.K. Suykens, Kernel Density Estimation for Dynamical Systems, Journal of Machine Learning Research, 19(35):1-49, 2018. [final]

H. Hang and I. Steinwart, A Bernstein-type inequality for some mixing processes and dynamical systems with an application to learning, Annals of Statistics, 45(2):708-743, 2017. [final]

H. Hang, Y. Feng, I. Steinwart, and J.A.K. Suykens, Learning Theory Estimates with Observations from General Stationary Stochastic Processes, Neural Computation, 28:2853-2889, 2016. [final]

Y. Feng, S.-G. Lv, H. Hang, and J.A.K. Suykens, Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery, Neural Computation, 28:525-562, 2016. [final]

H. Hang and I. Steinwart, Fast learning from alpha-mixing observations, Journal of Multivariate Analysis, 127:184-199, 2014. [final]

Google Scholar Link

Courses Academic Year  2023/2024

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses Academic Year  2022/2023

Contact Details

Visiting Address

University of Twente
Drienerlolaan 5
7522 NB Enschede
The Netherlands

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Mailing Address

University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands