CS&E Colloquium: Taming the Beast: Practical Theories for Responsible Learning

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker, Zhun Deng (Columbia University), will be giving a talk titled "Taming the Beast: Practical Theories for Responsible Learning".

Abstract

Modern digital systems powered by machine learning have permeated various aspects of society, playing an instrumental role in many high-stakes areas such as medical care and finance. Therefore, it is crucial to ensure that machine learning algorithms are deployed in a “responsible” way so that digital systems are more reliable, explainable, and aligned with societal values. In this talk, I will introduce my research on building practical theories to guide real-world responsible deployment of machine learning. First, I will introduce our recent work on distribution-free uncertainty quantification for a rich class of statistical functionals of quantile functions to avoid catastrophic outcomes and unfair discrimination in the deployment of black-box models. The power of our framework is shown by applications to large language models and medical care. Second, I will describe an extension of the previous framework to handle group-based fairness notions so as to protect every group that can be meaningfully identified from data. At the end, I will conclude my talk with future directions.

Biography

Zhun Deng is a postdoctoral researcher in Computer Science with Toniann Pitassi and Richard Zemel at Columbia University. Previously, he completed his Ph.D. from the Theory of Computation Group at Harvard University, where he was advised by Cynthia Dwork. His research investigates both the theoretical foundations and applications of reliable and responsible machine learning. His papers have won multiple honors at flagship machine learning conferences. His research has also been awarded with fundings from the Accelerating Foundation Models Research Program of Microsoft.