Active Learning and Selective Sensing


Robert Nowak

Department of Electrical and Computer Engineering

University of Wisconsin-Madison


Abstract: Traditional approaches to machine learning and statistical inference are passive, in the sense that all data are collected prior to analysis in a non-adaptive fashion. One can envision, however more active strategies in which information gleaned from previously collected data is used to guide the selection of new data. This talk discusses the emerging theory of such "active learning" methods. I will show that feedback between data analysis and data collection can be crucial for effective learning and inference. The talk will describe two active learning problems. First, I will consider binary-valued prediction (classification) problems, for which the prediction errors of passive learning methods can be exponentially larger than those of active learning. Second, I will discuss the role of active learning in the recovery of sparse signals in noise. I will show that certain weak, sparse patterns are imperceptible from passive measurements, but can be recovered perfectly using selective sensing.


Bio: Robert Nowak received the B.S. (with highest distinction), M.S., and Ph.D. degrees in electrical engineering from the University of Wisconsin-Madison in 1990, 1992, and 1995, respectively.  He was a Postdoctoral Fellow at Rice University in 1995-1996, an Assistant Professor at Michigan State University from 1996-1999, held Assistant and Associate Professor positions at Rice University from 1999-2003, and was a Visiting Professor at INRIA in 2001. Dr. Nowak is now the McFarland-Bascom Professor of Engineering at the University of Wisconsin-Madison.  He has served as an Associate Editor for the IEEE Transactions on Image Processing, and is currently an Associate Editor for the ACM Transactions on Sensor Networks and the Secretary of the SIAM Activity Group on Imaging Science. He has also served as a Technical Program Chair for the IEEE Statistical Signal Processing Workshop and the IEEE/ACM International Symposium on Information Processing in Sensor Networks. Dr. Nowak received the General Electric Genius of Invention Award in 1993, the National Science Foundation CAREER Award in 1997, the Army Research Office Young Investigator Program Award in 1999, the Office of Naval Research Young Investigator Program Award in 2000, and IEEE Signal Processing Society Young Author Best Paper Award in 2000.  His research interests include statistical signal processing, machine learning, imaging and network science, and applications in communications, bio/medical imaging, and systems biology.


Thursday, Oct. 8, 2009 2:00-3:00 pm EECS room 1005