Fei Sha

USC

 

Abstract
Abstract: Statistical machine learning has become an important driving force behind many application fields. By large, however, its theoretical underpinning has hinged on the stringent assumption that the learning environment is stationary. In particular, the data distribution on which statistical models are optimized is the same as the distribution to which the models are applied.

Real-world applications are far more complex than the pristine condition. For instance, computer vision systems for recognizing objects in images often suffer from significant performance degradation if they are evaluated on image datasets that are different from the dataset on which they are designed.

In this talk, I will describe our efforts in addressing this important challenge of building intelligent systems that are robust to distribution disparity. The central theme is to learn invariant features, cast as learning kernel functions and adapt probabilistic models across different distributions (i.e., domains). To this end, our key insight is to discover and exploit hidden structures in the data. These structures, such as manifolds and discriminative clusters, are intrinsic and thus resilient to distribution changes due to exogenous factors. I will present several learning algorithms we have proposed and demonstrate their effectiveness in pattern recognition tasks from computer vision.
This talk is based on the joint work with my students (Boqing Gong and Yuan Shi, both from USC) and our collaborator Prof. Kristen Grauman (U. of Texas, Austin).
Bio:
Fei Sha is the Jack Munushian Early Career Chair and an assistant professor at the University of Southern California, Dept. of Computer Science. His primary research interests are machine learning and its applications to speech and language processing, computer vision, and robotics. He had won outstanding student paper awards at NIPS 2006 and ICML 2004. He was selected as a Sloan Research Fellow in 2013, won an Army Research Office Young Investigator Award in 2012, and was a member of DARPA 2010 Computer Science Study Panel. He has a Ph.D from Computer and Information Science from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing, China).