Welcome...

H. Hang (Hanyuan)

Assistant Professor

About Me

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

WORK EXPERIENCE

  • 2021-Now  Assistant Professor, University of Twente, The Netherlands 
  • 2019-2021  Machine Learning Researcher, AI Lab, Samsung Research, China
  • 2017-2019  Assistant Professor, Renmin University, China
  • 2015-2017  Post-doc Researcher, Catholic University of Leuven, Belgium
  • 2010-2015  Ph.D, University of Stuttgart, Germany

Research

Preprints

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]  

H. Hang, T. Huang, Y. Cai, H. Yang, and Z. Lin, Gradient Boosted Binary Histogram Ensemble for Large-scale Regression, technical report, 2021. [preprint]

H. Hang, Histogram Transform Ensembles for Density Estimation, technical report, 2019. [preprint]

H. Hang, X. Liu, and I. Steinwart, Best-scored Random Forest Classification, technical report, 2019. [preprint]

H. Hang, Y. Cai, and H. Yang, Density-based Clustering with Best-scored Random Forest, technical report, 2019. [preprint]

H. Hang, Y. Chen, and J.A.K. Suykens, Two-stage Best-scored Random Forest for Large-scale Regression, technical report, 2019. [preprint]

 

Accepted 

H. Wen and H. Hang, Random Forest Density Estimation, In Proceedings of the 39th International Conference on Machine Learning, 2022. [preprint]

 

Published 

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, 2022. [final]

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, 2021. [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 SVMsApplied 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]

Publications

Recent
Liu, J. , Hang, H., Wang, B., Li, B., Wang, H., Tian, Y., & Shi, Y. (2021). GAN-CL: Generative Adversarial Networks for Learning from Complementary Labels. IEEE transactions on cybernetics. https://doi.org/10.1109/TCYB.2021.3089337
Wen, H., Cui, J. , Hang, H., Liu, J., Wang, Y., & Lin, Z. (2021). Leveraged Weighted Loss for Partial Label Learning. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research; Vol. 139). http://proceedings.mlr.press/v139/wen21a.html
Cui, J. , Hang, H., Wang, Y., & Lin, Z. (2021). GBHT: Gradient Boosting Histogram Transform for Density Estimation. In International Conference on Machine Learning, 18-24 July 2021 (Proceedings of machine learning research; Vol. 139).
Hang, H., & Steinwart, I. (2021). Optimal learning with anisotropic Gaussian SVMs. Applied and Computational Harmonic Analysis, 55, 337-367. https://doi.org/10.1016/j.acha.2021.06.004
Hang, H., Lin, Z., Liu, X., & Wen, H. (2021). Histogram Transform Ensembles for Large-scale Regression. Journal of machine learning research, 22. https://jmlr.org/papers/v22/19-1004.html

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