Fast learning from α-mixing observations. - Hanyuan Hang; Ingo Steinwart (J. Multivar. Anal., 2014)

Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery. - Yunlong Feng; Shao-Gao Lv; Hanyuan Hang; Johan A. K. Suykens (Neural Comput., 2016)

Learning Theory Estimates with Observations from General Stationary Stochastic Processes. - Hanyuan Hang; Yunlong Feng; Ingo Steinwart; Johan A. K. Suykens (Neural Comput., 2016)

Matrix Infinitely Divisible Series: Tail Inequalities and Applications in Optimization. - Chao Zhang 017; Xianjie Gao; Min-Hsiu Hsieh; Hanyuan Hang; Dacheng Tao (CoRR, 2018)

Optimal Learning with Anisotropic Gaussian SVMs. - Hanyuan Hang; Ingo Steinwart (CoRR, 2018)

Kernel Density Estimation for Dynamical Systems. - Hanyuan Hang; Ingo Steinwart; Yunlong Feng; Johan A. K. Suykens (J. Mach. Learn. Res., 2018)

Two-stage Best-scored Random Forest for Large-scale Regression. - Hanyuan Hang; Yingyi Chen; Johan A. K. Suykens (CoRR, 2019)

Best-scored Random Forest Density Estimation. - Hanyuan Hang; Hongwei Wen (CoRR, 2019)

Best-scored Random Forest Classification. - Hanyuan Hang; Xiaoyu Liu; Ingo Steinwart (CoRR, 2019)

Density-based Clustering with Best-scored Random Forest. - Hanyuan Hang; Yuchao Cai; Hanfang Yang (CoRR, 2019)

Histogram Transform Ensembles for Density Estimation. - Hanyuan Hang (CoRR, 2019)

Attention-Based Pedestrian Attribute Analysis. - Zichang Tan; Yang Yang 062; Jun Wan 001; Hanyuan Hang; Guodong Guo; Stan Z. Li (IEEE Trans. Image Process., 2019)

Boosted Histogram Transform for Regression. - Yuchao Cai; Hanyuan Hang; Hanfang Yang; Zhouchen Lin (ICML, 2020)

Matrix Infinitely Divisible Series: Tail Inequalities and Their Applications. - Chao Zhang 017; Xianjie Gao; Min-Hsiu Hsieh; Hanyuan Hang; Dacheng Tao (IEEE Trans. Inf. Theory, 2020)

GBHT: Gradient Boosting Histogram Transform for Density Estimation. - Jingyi Cui; Hanyuan Hang; Yisen Wang 001; Zhouchen Lin (ICML, 2021)

Leveraged Weighted Loss for Partial Label Learning. - Hongwei Wen; Jingyi Cui; Hanyuan Hang; Jiabin Liu; Yisen Wang 001; Zhouchen Lin (ICML, 2021)

Gradient Boosted Binary Histogram Ensemble for Large-scale Regression. - Hanyuan Hang; Tao Huang; Yuchao Cai; Hanfang Yang; Zhouchen Lin (CoRR, 2021)

Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learning. - Hanyuan Hang (CoRR, 2021)

Histogram Transform Ensembles for Large-scale Regression. - Hanyuan Hang; Zhouchen Lin; Xiaoyu Liu; Hongwei Wen (J. Mach. Learn. Res., 2021)

Random Forest Density Estimation. - Hongwei Wen; Hanyuan Hang (ICML, 2022)

Bagged k-Distance for Mode-Based Clustering Using the Probability of Localized Level Sets. - Hanyuan Hang (CoRR, 2022)

Under-bagging Nearest Neighbors for Imbalanced Classification. - Hanyuan Hang; Yuchao Cai; Hanfang Yang; Zhouchen Lin (J. Mach. Learn. Res., 2022)

Bagged Regularized k-Distances for Anomaly Detection. - Yuchao Cai; Yuheng Ma 001; Hanfang Yang; Hanyuan Hang (CoRR, 2023)

Class Probability Matching Using Kernel Methods for Label Shift Adaptation. - Hongwei Wen; Annika Betken; Hanyuan Hang (CoRR, 2023)

GAN-CL: Generative Adversarial Networks for Learning From Complementary Labels. - Jiabin Liu; Hanyuan Hang; Bo Wang 049; Biao Li; Huadong Wang; Yingjie Tian 001; Yong Shi 001 (IEEE Trans. Cybern., 2023)

LLP-GAN: A GAN-Based Algorithm for Learning From Label Proportions. - Jiabin Liu; Bo Wang 049; Hanyuan Hang; Huadong Wang; Zhiquan Qi; Yingjie Tian 001; Yong Shi 001 (IEEE Trans. Neural Networks Learn. Syst., 2023)

Class Probability Matching with Calibrated Networks for Label Shift Adaption. - Hongwei Wen; Annika Betken; Hanyuan Hang (ICLR, 2024)

Optimal Learning of Kernel Logistic Regression for Complex Classification Scenarios. - Hongwei Wen; Annika Betken; Hanyuan Hang (ICLR, 2025)

Self-Supervised Random Forest on Transformed Distribution for Anomaly Detection. - Jiabin Liu; Huadong Wang; Hanyuan Hang; Shumin Ma; Xin Shen 003; Yong Shi 001 (IEEE Trans. Neural Networks Learn. Syst., 2025)