<html><head><meta http-equiv="Content-Type" content="text/html; charset=us-ascii"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div class=""><span class="" style="font-size: 14px; orphans: 2; widows: 2;">UNIVERSITY OF CHICAGO</span><br class="" style="font-size: 14px; orphans: 2; widows: 2;"><span class="" style="font-size: 14px; orphans: 2; widows: 2;">DEPARTMENT OF COMPUTER SCIENCE</span></div><div class="" style="orphans: 2; widows: 2;"><span class="" style="font-size: 14px;">PRESENTS</span></div><div class="" style="orphans: 2; widows: 2;"><span class="" style="font-size: 14px;"><br class=""></span></div><div class="" style="orphans: 2; widows: 2;"><div class="" style="margin: 0px; line-height: normal;"><img apple-inline="yes" id="2BC88B68-D483-4057-8B71-45E7A91817C9" class="" src="cid:6C8730D7-7B3E-44B3-A450-1D7CB171EFD9@cs.uchicago.edu"></div></div><div class="" style="orphans: 2; widows: 2;"><div class="" style="margin: 0in 0in 0.0001pt;"><b class="" style="font-size: 14px;"><br class=""></b></div><div class="" style="margin: 0in 0in 0.0001pt;"><b class="" style="font-size: 14px;">Rebecca Willett</b></div><div class="" style="margin: 0in 0in 0.0001pt;"><span class=""><span class=""><span class="" style="font-size: 14px;"><i class="">University of Wisconsin-Madison</i></span></span></span></div><div class="" style="margin: 0in 0in 0.0001pt;"><br class=""></div><div class="" style="margin: 0in 0in 0.0001pt;"><b class=""><font class="" style="font-size: 14px;"><br class=""></font></b></div><div class="" style="margin: 0in 0in 0.0001pt;"><span class="" style="font-size: 14px;"><b class=""><font class="">Wednesday, March 28, 2018 at 2:30 pm <br class="">Ryerson 251</font></b><br class=""></span></div></div><div class="" style="orphans: 2; widows: 2;"><span class="" style="font-size: 14px;"><br class=""></span></div><div class=""><br class=""></div><div class=""><div class=""><b class="" style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;">Title: </b><span class="" style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;">Learning from Highly Correlated Features using Graph Total Variation</span></div><div class="" style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;"><b class=""><br class=""></b></div><div class="" style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;"><b class="">Abstract:</b></div><div class=""><font color="#212121" face="Roboto, Helvetica, Arial, sans-serif" class=""><span class=""><span class=""><span class=""><span class=""><span class="" style="font-size: 14px;">Sparse models for machine learning have received substantial attention over the past two decades. Model selection, or determining which features are the best explanatory variables, is critical to the interpretability of a learned model. Much of this work assumes that features are only mildly correlated. However, in modern applications ranging from functional MRI to genome-wide association studies, we observe highly correlated features that do not exhibit key properties (such as the restricted eigenvalue condition). In this talk, I will describe novel methods for robust sparse linear regression in these settings. Using side information about the strength of correlations among features, we form a graph with edge weights corresponding to pairwise correlations. This graph is used to define a graph total variation regularizer that promotes similar weights for highly correlated features. I will show how the graph structure encapsulated by this regularizer helps precondition correlated features to yield provably accurate estimates. The proposed approach outperforms several previous approaches in a variety of experiments on simulated and real fMRI data. This is joint work with Yuan Li and Garvesh Raskutti.<br class=""><br class=""> </span></span></span><span class="" style="font-size: 14px;"> </span></span></span><br class=""></font><b class="" style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;">Bio:</b></div><div class=""><span id="docs-internal-guid-8bdccc40-716c-e22d-6a5f-d3d1c5863489" class=""><div class="" style="line-height: 1.38; margin-top: 0pt; margin-bottom: 0pt;"><font color="#212529" class=""><span class="" style="font-size: 14px; white-space: pre-wrap;"><i class="">Rebecca Willett is an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. 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. Willett has also held visiting researcher or faculty positions at the University of Nice in 2015, 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<br class="">and Development Laboratory at GE Healthcare in 2002.</i></span></font></div><div class="" style="font-size: 14px; white-space: pre-wrap; line-height: 1.38; margin-top: 0pt; margin-bottom: 0pt;"><font color="#212529" class=""><i class=""><br class=""></i></font></div></span></div><div class=""><div class="" style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;"><b class="">Host: Michael Franklin</b></div><div class="" style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;"><br class=""></div><div class="" style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif;"><i class=""><font size="2" class="">Refreshments served after the talk in Ry. 255</font></i></div></div></div></body></html>