The real world is open and full of unknowns, presenting significant challenges for machine learning (ML) systems that must reliably handle diverse, and sometimes anomalous inputs. Out-of-distribution (OOD) uncertainty arises when a machine learning model sees a test-time input that differs from its training data, and thus should not be predicted by the model. As ML is used for more safety-critical domains, the ability to handle out-of-distribution data are central in building open-world learning systems. In this talk, I will talk about challenges, methods, and opportunities on uncovering the unknowns of deep neural networks for reliable predictions in an open world.
Sharon Yixuan Li is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Previously she was a postdoc research fellow in the Computer Science department at Stanford AI Lab. She completed her Ph.D. from Cornell University in 2017, where she was advised by John E. Hopcroft. She leads the organization of the ICML Workshop on Uncertainty and Robustness in Deep Learning in 2019 and 2020, and has served as area chair for NeurIPS, ICML, ICLR, and AAAI. Her broad research interests are in deep learning and machine learning. Her research develops algorithms and fundamental understandings to enable reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data streams. She is the recipient of the Facebook Research Award, JPMorgan early-career faculty award, Madison Teaching and Learning Excellence fellowship, and was named Forbes 30Under30 in Science. Website: http://pages.cs.wisc.edu/~sharonli/