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<div dir="auto" style="color:rgb(0,0,0)"><font face="Arial"><b style="font-size:14px"><span id="gmail-m_66427368400451387m_7082043948795332671gmail-m_3918681043343225718m_7776154040874388940gmail-docs-internal-guid-d34d626a-7fff-fa18-31b7-15dad8158c9a"><p style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;line-height:normal;margin:0px"><font face="arial, sans-serif"><font size="2"><span style="font-weight:normal"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>When: </b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit"> Friday, January 24th at 10:30am</font></font></span></font><br></font></p></span></b></font><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Where:</b> </font></font><font style="vertical-align:inherit"><font style="vertical-align:inherit">TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526</font></font></font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"> </font></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><font face="arial, sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b> </font></font></font>Steve Hanneke, TTIC</p><font><font style="font-family:Arial" color="#000000"><span style="font-size:14px"></span></font><br><b style="font-family:Arial;font-size:14px">Title:</b></font><span 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" style="color:rgb(0,0,0)"><font><b style="font-family:Arial"><font style="font-weight:normal" face="Arial"><b style="font-size:15.3333px"><br></b></font></b></font></div><div dir="auto" style="overflow-wrap: break-word;"><font face="Arial"><span><font style="color:rgb(0,0,0);font-size:14px" face="Arial"><b>Abstract:</b><br></font><div><font color="#000000"><span 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 style="color:rgb(0,0,0)"><font color="#000000"><span style="font-size:14px"><b><br></b></span></font></div><div style="color:rgb(0,0,0)"><font color="#000000"><span style="font-size:14px"><b>Bio:</b></span></font></div><div><font color="#000000"><span 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 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 foundations of
active learning.</span></font></div><div style="color:rgb(0,0,0)"><font color="#000000"><span><span><b style="font-size:14px"><br></b></span></span></font></div><div style="color:rgb(0,0,0)"><font color="#000000"><span><span><b style="font-size:14px">Host: Brian Bullins</b></span></span></font></div></span></font></div>
</div><div><br></div><div><br></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif"><b>Jerome Allen</b><br></font><div><font face="arial, helvetica, sans-serif">Executive Assistant<br></font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 518</font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></div><div><font face="arial, helvetica, sans-serif">p:(773) 702-2311<br></font></div><div><i><b><a href="mailto:jallen@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">jallen@ttic.edu</font></a></b></i></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>