题　目：Harmonizing Fairness with Utility in Data and Learning

嘉　宾：Hongfu Liu, Assistant Professor, Brandeis University

主持人：魏煊  助理教授  tyc1286太阳成集团

时　间：2023年12月13日（周三）14:00-15:30

地　点：tyc1286太阳成集团 徐汇校区安泰楼A407室

内容简介:

Utility-oriented machine learning systems have made their way into the real world, assisting with high-stakes decision-making in areas such as college admissions, loan approvals, and credit auditing. However, the inherent biases embedded in these models, stemming from unbalanced data, can backfire and worsen existing social barriers. Fairness in machine learning has gained significant attention to tackle the above algorithmic discrimination in diverse tasks. Among these literature, mitigating unfairness can be achieved through three main categories of solutions: pre-processing, in-processing, and post-processing. In this talk, I will introduce our recent studies on fairness machine learning of learning and data perspectives.

演讲人简介：

Dr. Hongfu Liu is an Assistant Professor of Computer Science at Brandeis University. His research interests lie in core machine learning and AI-assisted applications. The PI has published over 100 papers (e.g., KDD, NeurIPS, ICLR, ICML, IJCAI, AAAI, ICDM, SDM, CIKM, CVPR, ICCV, TPAMI, and TKDE). These publications have received over 3,000 citations with an h-index of 32 according to Google Scholar as of Nov. 2023. He has also won several awards including the First Place Award in MS-Celel-1M Grand Challenge in ICCV 2017, the NVIDIA CCS Best Student Paper Award in FG 2021, the 2021 INNS Aharon Katzir Young Investigator Award, the top reviewer in UAI 2022, the highlighted Area Chair in ICLR 2022, the 2022 Global Top-25 Chinese Young Scholars in AI (Data Mining Area) by Baidu Scholar and the notable Area Chair in ICLR 2023. He has served as an Associate Editor of IEEE CIM and as an Area Chair of ICLR, ICML, and NeurIPS.

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