Dynamical models and tracking regret in online convex programming
Duke University, Department of Electrical and Computer Engineering
Thursday, April 18, 2013|
4:00pm - 5:00pm
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About the Event
Modern sensors are collecting data at unprecedented rates, often from platforms with limited processing power and bandwidth for data transmission. To cope with this data deluge, we must develop robust methods for efficiently extracting information from large-scale streaming data. Online optimization methods are often designed to have a total accumulated loss comparable to that achievable by some comparator, such as a batch algorithm with access to all the data and infinite computational resources; the associated "regret" bounds scale with the overall variation of the comparator sequence. However, in practical scenarios ranging from motion imagery to network analysis, the environment is nonstationary and comparator sequences with small variation are quite weak, resulting in large losses. I will describe a "dynamic mirror descent" method which addresses this challenge, yielding low regret relative to highly variable comparator sequences by both tracking the best dynamical model and forming predictions based on that model. This concept is demonstrated empirically in the context of sequential compressive observations of a dynamic scene and tracking a dynamic social network.
Rebecca Willett is an associate professor in the Electrical and Computer Engineering Department at Duke University. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005. Prof. Willett received the National Science Foundation CAREER Award in 2007, is a member of the DARPA Computer Science Study Group, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. Prof. Willett has also held visiting researcher positions at the Institute for Pure and Applied Mathematics at UCLA in 2004, the University of Wisconsin-Madison 2003-2005, the French National Institute for Research in Computer Science and Control (INRIA) in 2003, and the Applied Science Research and Development Laboratory at GE Healthcare in 2002. Her research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. Additional information, including publications and software, are available online at http://www.ee.duke.edu/~willett/.
Contact: Ann Pace
Sponsor(s): University of Michigan
Open to: Public