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 Adaptation, technical report, 2023. [preprint]

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

H. Hang, Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets, technical 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 Learning, technical 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 Classification, Journal 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]

Research profiles

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