Neural tangent kernels, transportation mappings, and universal approximation. - Ziwei Ji; Matus Telgarsky; Ruicheng Xian (ICLR, 2020)

Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations. - Ruicheng Xian; Heng Ji 001; Han Zhao 002 (ICLR, 2022)

Fair and Optimal Classification via Transports to Wasserstein-Barycenter. - Ruicheng Xian; Lang Yin; Han Zhao 002 (CoRR, 2022)

Fair and Optimal Classification via Post-Processing. - Ruicheng Xian; Lang Yin; Han Zhao 002 (ICML, 2023)

Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective. - Yuzheng Hu; Ruicheng Xian; Qilong Wu 002; Qiuling Fan; Lang Yin; Han Zhao 002 (NeurIPS, 2023)

Learning List-Level Domain-Invariant Representations for Ranking. - Ruicheng Xian; Honglei Zhuang; Zhen Qin 001; Hamed Zamani; Jing Lu; Ji Ma 004; Kai Hui 001; Han Zhao 002; Xuanhui Wang; Michael Bendersky (NeurIPS, 2023)

Differentially Private Post-Processing for Fair Regression. - Ruicheng Xian; Qiaobo Li; Gautam Kamath 001; Han Zhao 002 (ICML, 2024)

Optimal Group Fair Classifiers from Linear Post-Processing. - Ruicheng Xian; Han Zhao 002 (CoRR, 2024)

Empirical Privacy Variance. - Yuzheng Hu; Fan Wu 011; Ruicheng Xian; Yuhang Liu; Lydia Zakynthinou; Pritish Kamath; Chiyuan Zhang; David A. Forsyth (CoRR, 2025)

Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing. - Ruicheng Xian; Yuxuan Wan; Han Zhao (CoRR, 2025)