The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization with Outliers. - Hu Ding; Ruizhe Qin; Jiawei Huang 009 (CoRR, 2020)

Defending Support Vector Machines against Poisoning Attacks: the Hardness and Algorithm. - Hu Ding; Fan Yang; Jiawei Huang 009 (CoRR, 2020)

A Novel Sequential Coreset Method for Gradient Descent Algorithms. - Jiawei Huang 009; Ruomin Huang; Wenjie Liu 008; Nikolaos M. Freris; Hu Ding (ICML, 2021)

Defending SVMs against poisoning attacks: the hardness and DBSCAN approach. - Hu Ding; Fan Yang; Jiawei Huang 009 (UAI, 2021)

Is Simple Uniform Sampling Efficient for Center-Based Clustering With Outliers: When and Why? - Hu Ding; Jiawei Huang 009 (CoRR, 2021)

Coresets for Wasserstein Distributionally Robust Optimization Problems. - Ruomin Huang; Jiawei Huang 009; Wenjie Liu 008; Hu Ding (NeurIPS, 2022)

Random Projection and Recovery for High Dimensional Optimization with Arbitrary Outliers. - Jiawei Huang 009; Ruizhe Qin; Fan Yang; Hu Ding (Int. J. Comput. Geom. Appl., 2022)

Bi-criteria Sublinear Time Algorithms for Clustering with Outliers in High Dimensions. - Jiawei Huang 009; Wenjie Liu 008; Hu Ding (COCOON (1), 2024)

On Robust Wasserstein Barycenter: The Model and Algorithm. - Xu Wang 030; Jiawei Huang 009; Qingyuan Yang; Jinpeng Zhang (SDM, 2024)

An Effective Manifold-based Optimization Method for Distributionally Robust Classification. - Jiawei Huang 009; Hu Ding (ICLR, 2025)