How to Handle Data Shifts in the Wild? Challenges, Research Progress and Path Forward (virtual talk)

How to Handle Data Shifts in the Wild? Challenges, Research Progress and Path Forward (via Zoom). Virtual talk

Abstract: When deploying machine learning models in the open and non-stationary world, their reliability is often challenged by the presence of out-of-distribution (OOD) samples. Since data shifts happen prevalently in the real world, identifying OOD inputs has become an important problem in machine learning. In this talk, I will discuss challenges, research progress, and opportunities in OOD detection. Our work is motivated by the insufficiency of existing learning objective such as ERM --- which focuses on minimizing error only on the in-distribution (ID) data, but do not explicitly account for the uncertainty that arises outside ID data. To mitigate the fundamental limitation, I will introduce a new algorithmic framework of open-world risk minimization, which jointly optimizes for both accurate classification of ID samples, and reliable detection of OOD data. The learning framework integrates distributional uncertainty as a first-class construct in the learning process, thus enabling both accuracy and safety guarantees.

Bio: Sharon Yixuan Li is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Her research focuses on learning and inference under distributional shifts and open-world machine learning. Previously, she was a postdoc fellow in the Computer Science department at Stanford University. She completed her Ph.D. at Cornell University, advised by John E. Hopcroft. She has served as Area Chair for ICLR, NeurIPS, ICML, and Program Chair for Workshop on Uncertainty and Robustness in Deep Learning. She is the recipient of the AFOSR Young Investigator Award (YIP), Forbes 30under30 in Science, and multiple faculty research awards from Google, Meta, and Amazon. Her works also received a NeurIPS Outstanding Paper Award, and an ICLR Outstanding Paper Award Honorable Mention in 2022.