<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=""><div dir="auto" class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><b class="" style="font-size: 14px;">University of Chicago </b></font><b class="" style="font-family: Arial; font-size: 14px;">and Toyota Technological Institute at Chicago</b></div><b class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); font-family: Arial; font-size: 14px;">Machine Learning Seminar Series</b><br class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); font-family: Arial;"><br class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"><div dir="auto" class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><br class=""><b class="" style="font-size: 14px;">Steve Hanneke, Assistant Professor</b></font></div><div dir="auto" class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><span class="" style="font-size: 14px;"><b class="">TTIC</b></span></font></div><div dir="auto" class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); word-wrap: break-word; line-break: after-white-space;"><font class=""><br class=""><font color="#000000" class="" style="font-family: Arial;"><span class="" style="font-size: 14px;"><b class="">Friday, January 24, 10:30 - 11:30 am<br class=""></b></span></font><font face="Arial" class="">TTIC Rm 526</font></font></div><div dir="auto" class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); word-wrap: break-word; line-break: after-white-space;"><font class=""><font color="#000000" class=""><span class="">(Remote broadcast at JCL Rm 390)</span></font><br class=""><br class=""><b class="" style="font-family: Arial; font-size: 14px;">Title:</b></font></div><div dir="auto" class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); word-wrap: break-word; line-break: after-white-space;"><span class="" style="font-size: 12pt; font-family: "Arial Narrow", sans-serif;">Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes</span> </div><div dir="auto" class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); word-wrap: break-word; line-break: after-white-space;"><font class=""><b class="" style="font-family: Arial;"><font face="Arial" class="" style="font-weight: normal;"><b class="" style="font-size: 15.333333015441895px;"><br class=""></b></font></b></font></div><div dir="auto" class="" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><span class=""><font face="Arial" class="" style="font-size: 14px;"><b class="">Abstract:</b><br class=""></font><div class=""><font color="#000000" class=""><span class="" style="font-size: 14px;">I will present a general theory of learning and generalization without the i.i.d. assumption, starting from first principles. We focus on the problem of universal consistency: the ability to estimate any function from X to Y. We endeavor to answer the question: Does there exist a learning algorithm that is universally consistent under every process on X for which universal consistency is possible? Remarkably, we find the answer is "Yes". Thus, we replace the i.i.d. assumption with only the most natural (and necessary!) assumption: that learning is at least possible. Along the way, we also find a concise characterization of the family of all processes that admit universally consistent learners. One challenge for learning is that some of these processes do not even have a law of large numbers.</span></font></div><div class=""><font color="#000000" class=""><span class="" style="font-size: 14px;"><b class=""><br class=""></b></span></font></div><div class=""><font color="#000000" class=""><span class="" style="font-size: 14px;"><b class="">Bio:</b></span></font></div><div class=""><font color="#000000" class=""><span class="" style="font-size: 14px;">Steve Hanneke is a Research Assistant Professor at the Toyota Technological Institute at Chicago. His research explores the theory of machine learning, with a focus on reducing the number of training examples sufficient for learning. His work develops new approaches to<br class="">supervised, semi-supervised, transfer, and active learning, and also revisits the basic probabilistic assumptions at the foundation of learning theory. Steve earned a Bachelor of Science degree in Computer Science from UIUC in 2005 and a Ph.D. in Machine Learning from Carnegie Mellon University in 2009 with a dissertation on the theoretical foundatio</span></font><span style="font-size: 14px;" class="">ns of active learning.</span></div></span></font><div class=""><div dir="auto" class="" style="word-wrap: break-word; line-break: after-white-space;"><font face="Arial" class=""><span class=""><div class=""><font class="" color="#000000"><span class=""><span class=""><b class="" style="font-size: 14px;"><br class=""></b></span></span></font></div><div class=""><font class="" color="#000000"><span class=""><span class=""><b class="" style="font-size: 14px;">Host: Brian Bullins</b></span></span></font></div><div class=""><font class="" color="#000000"><span class=""><span class=""><b class="" style="font-size: 14px;"><br class=""></b></span></span></font></div></span></font></div></div></div></div><div class=""><br class=""></div><div class=""></div></body></html>