I received my PhD from the University of Illinois Urbana-Champaign, where I was fortunate to be advised by Han Zhao, and to have worked with Matus Telgarsky. I am now at OpenAI.
My interests lie broadly in machine learning; my thesis studied algorithmic fairness.
Papers
Empirical Privacy Variance
Yuzheng Hu, Fan Wu, Ruicheng Xian, Yuhang Liu, Lydia Zakynthinou, Pritish Kamath, Chiyuan Zhang, David Forsyth
ICML 2025 ⋅ pdf
A Unified Post-Processing Framework for Group Fairness in Classification
Ruicheng Xian, Han Zhao
pdf ⋅ code
Differentially Private Post-Processing for Fair Regression
Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao
ICML 2024 ⋅ pdf ⋅ code
Learning List-Level Domain-Invariant Representations for Ranking
Ruicheng Xian, Honglei Zhuang, Zhen Qin, Hamed Zamani, Jing Lu, Ji Ma, Kai Hui, Han Zhao, Xuanhui Wang, Michael Bendersky
NeurIPS 2023 spotlight ⋅ pdf ⋅ code
Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective
Yuzheng Hu, Ruicheng Xian, Qilong Wu, Qiuling Fan, Lang Yin, Han Zhao
NeurIPS 2023 ⋅ pdf
Fair and Optimal Classification via Post-Processing
Ruicheng Xian, Lang Yin, Han Zhao
ICML 2023 ⋅ pdf ⋅ code
Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations
Ruicheng Xian, Heng Ji, Han Zhao
ICLR 2022 ⋅ pdf ⋅ code
Neural tangent kernels, transportation mappings, and universal approximation
Ziwei Ji, Matus Telgarsky, Ruicheng Xian
ICLR 2020 ⋅ pdf
I like to draw, am a heavy coffee drinker/enjoyer, and my go-to for lunch is Cantonese-style BBQ.
