10 citations · 23 across the 6 of their papers we have counts for
6 papers · 1 filter
Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing
Ruicheng Xian, Yuxuan Wan, Han Zhao
Instruction fine-tuned large language models (LLMs) enable a simple zero-shot or few-shot prompting paradigm, also known as in-context learning, for building prediction models. Thi…
Differentially Private Post-Processing for Fair Regression
Ruicheng Xian, Qiaobo Li, Gautam Kamath +1
This paper describes a differentially private post-processing algorithm for learning fair regressors satisfying statistical parity, addressing privacy concerns of machine learning…
A Unified Post-Processing Framework for Group Fairness in Classification
Ruicheng Xian, Han Zhao
We present a post-processing algorithm for fair classification that covers group fairness criteria including statistical parity, equal opportunity, and equalized odds under a singl…
Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective
Yuzheng Hu, Ruicheng Xian, Qilong Wu +3
Linear scalarization, i.e., combining all loss functions by a weighted sum, has been the default choice in the literature of multi-task learning (MTL) since its inception. In recen…
Fair and Optimal Classification via Post-Processing
Ruicheng Xian, Lang Yin, Han Zhao
To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it of…
Neural tangent kernels, transportation mappings, and universal approximation
Ziwei Ji, Matus Telgarsky, Ruicheng Xian
This paper establishes rates of universal approximation for the shallow neural tangent kernel (NTK): network weights are only allowed microscopic changes from random initialization…