Sharon Li: How to Handle Data Shifts? Challenges, Research Progress and Path Forward скачать в хорошем качестве

Sharon Li: How to Handle Data Shifts? Challenges, Research Progress and Path Forward

Abstract: The real world is open and full of unknowns, presenting significant challenges for machine learning 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 machine learning is used for more safety-critical domains, the ability to handle out-of-distribution data is central in building open-world learning systems. In this talk, I will talk about challenges, research progress, and future opportunities in detecting OOD samples for safe and reliable predictions in an open world. Bio: Sharon Yixuan Li is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin Madison. Her broad research interests are in deep learning and machine learning. Her research focuses on learning and inference under distributional shifts and open-world machine learning. 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 led the organization of the ICML workshop on Uncertainty and Robustness in Deep Learning in 2019 and 2020. She is the recipient of several awards, including the Facebook Research Award, Amazon Research Award, and was named Forbes 30Under30 in Science.