Online Learning with Samples Drawn from Non-identical Distributions. - Ting Hu 002; Ding-Xuan Zhou (J. Mach. Learn. Res., 2009)

Learning with varying insensitive loss. - Dao-Hong Xiang; Ting Hu 002; Ding-Xuan Zhou (Appl. Math. Lett., 2011)

Approximation Analysis of Learning Algorithms for Support Vector Regression and Quantile Regression. - Dao-Hong Xiang; Ting Hu 002; Ding-Xuan Zhou (J. Appl. Math., 2012)

Learning theory approach to minimum error entropy criterion. - Ting Hu 002; Jun Fan; Qiang Wu 003; Ding-Xuan Zhou (J. Mach. Learn. Res., 2013)

Consistency Analysis of an Empirical Minimum Error Entropy Algorithm. - Jun Fan; Ting Hu 002; Qiang Wu 003; Ding-Xuan Zhou (CoRR, 2014)

Convergence of Gradient Descent for Minimum Error Entropy Principle in Linear Regression. - Ting Hu 002; Qiang Wu 003; Ding-Xuan Zhou (IEEE Trans. Signal Process., 2016)

Semi-Supervised Minimum Error Entropy Principle with Distributed Method. - Baobin Wang; Ting Hu 002 (Entropy, 2018)

Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions. - Yunwen Lei; Ting Hu 002; Ke Tang 001 (CoRR, 2019)

Online Gradient Descent for Kernel-Based Maximum Correntropy Criterion. - Baobin Wang; Ting Hu 002 (Entropy, 2019)

Distributed pairwise algorithms with gradient descent methods. - Baobin Wang; Ting Hu 002 (Neurocomputing, 2019)

Deep Learning on Point Clouds and Its Application: A Survey. - Weiping Liu; Jia Sun; Wanyi Li; Ting Hu 002; Peng Wang 024 (Sensors, 2019)

Distributed Minimum Error Entropy Algorithms. - Xin Guo 003; Ting Hu 002; Qiang Wu 003 (J. Mach. Learn. Res., 2020)

Stochastic Gradient Descent for Nonconvex Learning Without Bounded Gradient Assumptions. - Yunwen Lei; Ting Hu 002; Guiying Li; Ke Tang 001 (IEEE Trans. Neural Networks Learn. Syst., 2020)

Kernel gradient descent algorithm for information theoretic learning. - Ting Hu 002; Qiang Wu 003; Ding-Xuan Zhou (J. Approx. Theory, 2021)

Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions. - Yunwen Lei; Ting Hu 002; Ke Tang 001 (J. Mach. Learn. Res., 2021)

Early Stopping for Iterative Regularization with General Loss Functions. - Ting Hu 002; Yunwen Lei (J. Mach. Learn. Res., 2022)

Pairwise learning problems with regularization networks and Nyström subsampling approach. - Cheng Wang; Ting Hu 002; Siyang Jiang (Neural Networks, 2023)