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Tuesday, Sep 13, 2022

Time: 12:00 - 01:00 PM ET
Recording of this Online Seminar on Youtube

Zico Kolter -- New approaches to detecting and adapting to domain shifts in machine learning

Relevant Paper(s):

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Abstract: Machine learning systems, in virtually every deployed system, encounter data from a qualitatively different distribution than what they were trained upon. Effectively dealing with this problem, known as domain shift, is thus perhaps the key challenge in deploying machine learning methods in practice. In this talk, I will motivate some of these challenges in domain shift, and highlight some of our recent work on two topics. First, I will present our work on determining if we can even just evaluate the performance of machine learning models under distribution shift, without access to labelled data. And second, I will present work on how we can better adapt our classifiers to new data distributions, again assuming access only to unlabelled data in the new domain.

Bio: Zico Kolter is an Associate Professor in the Computer Science Department at Carnegie Mellon University, and also serves as chief scientist of AI research for the Bosch Center for Artificial Intelligence. His work spans the intersection of machine learning and optimization, with a large focus on developing more robust and rigorous methods in deep learning. In addition, he has worked in a number of application areas, highlighted by work on sustainability and smart energy systems. He is a recipient of the DARPA Young Faculty Award, a Sloan Fellowship, and best paper awards at NeurIPS, ICML (honorable mention), IJCAI, KDD, and PESGM.