主题：Statistical Foundations for Trustworthy AI: Fairness, Uncertainty, and Online Inference
主讲人：孔令龙 加拿大阿尔伯塔大学
主持人：杨广仁 暨南大学
时间：2025年11月18日（周二）下午16:00-17:30
地点：暨南大学石牌校区经济学院大楼（中惠楼）323室
摘要
Ensuring fairness, reliability, and transparency is central to trustworthy artificial intelligence. We present statistical frameworks that address these challenges from fairness and uncertainty quantification perspectives. First, we propose a method for fair quantile prediction that leverages optimal transport and Wasserstein barycenters to enforce demographic parity across sensitive groups, capturing disparities often hidden in mean-based approaches. We extend this to Conformal Fair Quantile Prediction (CFQP), which delivers prediction intervals with exact coverage guarantees while significantly reducing group-level bias. Second, we develop online inference procedures for smoothed quantile regression, including renewable estimators and debiased lasso methods, enabling valid statistical inference with streaming, high-dimensional, or heavy-tailed data. These contributions highlight how modern statistical methodology—integrating quantile regression, conformal inference, and online learning—provides rigorous foundations for AI systems deployed in socially sensitive, high-stakes domains.
主讲人简介
孔令龙，阿尔伯塔大学数学与统计科学系教授、统计学习加拿大研究主席和加拿大CIFAR 人工智能主席美国统计协会(ASA)和阿尔伯塔机器智能研究所(Amii)会士。在 AOS、JASA、JRSSB、NeurlPS、ICML 和 ICLR等顶级期刊和会议上发表超过 120 篇同行评审论文。获2025年加拿大研究卓越奖(CRM-SSC Prize)。担任《JASA》和《AOAS》等多个顶级期刊的副编辑，在美国统计协会和加拿大统计学会中担任领导职务。研究兴趣包括高维数据分析、神经影像数据分析、统计机器学习、稳健统计、分位数回归、可信机器学习以及面向智慧健康的AI技术。
欢迎感兴趣的师生参加！
校对| 朱海斌
责编| 彭毅
初审| 杨广仁
终审发布| 何凌云
（来源：暨南大学经济学院微信公众号）
