报告题目：
Fairness-Constrained Optimal Model Averaging with High-Dimensional Sparsity Learning
报告时间：2025年12月18日（周四） 14:00-15:30
报告地点：腾讯会议 753-401-051
报告摘要：
Model fairness issues have received considerable attention recently in themachine learning community. The popular class of generalized linearmodels, such as logistic regression, has seen various developments toimprove fairness. Despite progress, studies remain very limited in high-dimensional settings without adequateunderstanding from statistical perspectives. In this work, we propose a novelfairness-constrained model averaging approach for generalized linearmodels that can aggregate a large number of sparse model candidates to generateasymptotically fair modeling solutions. It is shown toachieve near-optimal estimation risk in combining for estimationimprovement, including flexible scenarios where a true model is notamong the feasible candidates or is not necessarily representable by a single sparse model. To facilitate practicalapplications for the model averaging approach, we further propose a new fairness-assisted stepwise sparsitylearning method to help generate potentially fair model candidates. In addition, the fairness-assisted stepwise method withmodel selection maintainsconsistency properties when the true model is among the sparse feasiblecandidates, showing a delicate distinction of combining for estimationimprovement versus adaptation. The use of the proposed approaches is demonstrated through a real data analysis using a community crime dataset to improve fairness in high-dimensional modeling practice. The proposed approach is implemented in R and is publicly available on GitHub.
报告人简介：
Bintong Chen is the Chaplin Tyler Professor of the Lerner College of Business and Economics and the director of the Institute for Financial Services Analytics at the University of Delaware. His research focuses on optimization, data science, business and fintech applications, and his research work has been widely cited. He received many outstanding research and teaching awards in institutions he worked.
Professor Chen consulted many international companies, including JP Morgan Chase, Agriculture Bank of China, AT&T, Burlington Northern Rail, Delaware Department of Transportation, Nordstrom, and AstraZeneca, etc. He was a board member of APICS, the largest supply chain professional association in North American.
He graduated from Shanghai Jiaotong University with dual B.S. degrees in shipbuilding/naval architecture and electrical engineering. He received M.S. in systems engineering and Ph.D. in operations management/research from the Wharton School, the University of Pennsylvania.
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