How to Detect Out-of-Distribution Data in the Wild?
Sharon Y. Li, Assistant Professor in the Department of Computer Sciences, University of Wisconsin-Madison
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 objectives such as ERM --- which focuses on minimizing error only on the in-distribution (ID) data, but does not explicitly account for the uncertainty that arises outside ID data. To mitigate the fundamental limitation, I will introduce a new algorithmic framework, 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.
Cost: free