报告时间：2023年11月8日

               上午15:00-16:00

报告地点: 中国人民大学明德主楼1016

报告嘉宾：Dr. Bei Jiang

报告主题：Conformalized Fairness via Quantile Regression

报告摘要

Algorithmic fairness has received increased attention in socially sensitive domains. While rich literature on mean fairness has been established, research on quantile fairness remains sparse but vital. To fulfill great needs and advocate the significance of quantile fairness, we propose a novel framework to learn a real-valued quantile function under the fairness requirement of Demographic Parity with respect to sensitive attributes, such as race or gender, and thereby derive a reliable fair prediction interval. Using optimal transport and functional synchronization techniques, we establish theoretical guarantees of distribution-free coverage and exact fairness for the induced prediction interval constructed by fair quantiles. A hands-on pipeline is provided to incorporate flexible quantile regressions with an efficient fairness adjustment post-processing algorithm. We demonstrate the superior empirical performance of this approach on several benchmark datasets.

作者简介

Dr. Bei Jiang is an Associate Professor at the Department of Mathematical and Statistical Sciences of the University of Alberta, a fellow of the Alberta Machine Intelligence Institute and a Canada CIFAR AI chair. She received her PhD in Biostatistics in 2014 from University of Michigan. Prior to joining the University of Alberta in 2015 as an Assistant Professor, she was a postdoctoral researcher at the Department of Biostatistics at the Columbia University from 2014 to 2015. Her main research interests focus on statistical integration of multi-source and multi-modal data, and statistical learning methods for privacy and fairness. She has also worked closely with collaborators in women’s health, mental health, neurology, and industry partners to apply cutting-edge statistical learning methods to real-world applications.