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

Coresets for Relational Data and The Applications. - Jiaxiang Chen; Qingyuan Yang; Ruomin Huang; Hu Ding (NeurIPS, 2022)

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

The Impact of the Volume-Based Procurement Policy on Organizational Innovation Investment: Evidence from Chinese A-share Pharmaceutical Firms. - Ruomin Huang; Yukun Feng; Yumin Cao (DMI, 2023)

Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers. - Hu Ding; Ruomin Huang; Kai Liu 001; Haikuo Yu; Zixiu Wang (CoRR, 2023)

ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods. - Roy Xie; Junlin Wang; Ruomin Huang; Minxing Zhang; Rong Ge; Jian Pei 001; Neil Gong 001; Bhuwan Dhingra (EMNLP, 2024)

An Effective Dynamic Gradient Calibration Method for Continual Learning. - Weichen Lin; Jiaxiang Chen; Ruomin Huang; Hu Ding (ICML, 2024)

Task Descriptors Help Transformers Learn Linear Models In-Context. - Ruomin Huang; Rong Ge (ICLR, 2025)

To Tackle Adversarial Transferability: A Novel Ensemble Training Method with Fourier Transformation. - Wanlin Zhang; Weichen Lin; Ruomin Huang; Shihong Song; Hu Ding (ICLR, 2025)