Place: Chamberlin 5280 (Zoom link also available for online participants who signed up on our mailing list)
Speaker: Sharon Li, University of Wisconsin-Madison
Abstract: 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 decision-making in an open world.