<html><head><meta http-equiv="Content-Type" content="text/html charset=us-ascii"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;"><span style="font-family: Calibri, sans-serif; font-size: 11pt;">&nbsp;</span><br><div><blockquote type="cite"><div><div lang="EN-US" link="blue" vlink="purple"><div class="WordSection1"><p class="MsoNormal"><img width="480" height="160" id="Picture_x0020_1" alt="cid:image001.jpg@01D033CE.924ACE80" apple-inline="yes" apple-width="yes" apple-height="yes" src="cid:image001.jpg@01D046A6.160BC550"><o:p></o:p></p>
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<strong><i>Speaker:</i></strong><i>&nbsp;</i><em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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Dr. Guy Bresler <span style="mso-fareast-language:KO"><o:p></o:p></span></p>
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<strong><i>Title:</i></strong><i>&nbsp;</i><em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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Learning&nbsp;statistical structure in data: efficient estimation of&nbsp;graphical&nbsp;models<span style="font-size:12.0pt;mso-fareast-language:KO"><o:p></o:p></span></p>
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<strong><i>Day:</i></strong><i>&nbsp;</i><em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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<span style="">Thursday, February 19, 2015</span><em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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<em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal">&nbsp;</span></em></p>
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<em><b><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal">&nbsp;</span></b></em></p>
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<strong><i>Time:</i></strong><strong><i><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO"><o:p></o:p></span></i></strong></p>
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<em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;font-style:normal">4:30 pm</span></em><em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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<strong><i>Room:</i></strong><i>&nbsp;</i><em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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<em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;font-style:normal">B205, E-Quad</span></em><em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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<strong><i>Host:</i></strong><strong><i><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO"><o:p></o:p></span></i></strong></p>
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<em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;font-style:normal">Prof. Emmanuel Abbe</span></em><em><span style="font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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</table><p class="MsoNormal" style="text-align:justify"><span style="mso-fareast-language:KO">&nbsp;</span></p><p class="MsoNormal" style="text-align:justify"><b>&nbsp;</b></p><p class="MsoNormal" style="text-align:justify"><b>&nbsp;</b></p><p class="MsoNormal" style="text-align:justify"><b>&nbsp;</b></p><p class="MsoNormal" style="text-align:justify"><b>&nbsp;</b></p><p class="MsoNormal" style="text-align:justify"><b>&nbsp;</b></p><p class="MsoNormal" style="text-align:justify"><b>&nbsp;</b></p><p class="MsoNormal" style="text-align:justify"><b>&nbsp;</b></p><p class="MsoNormal" style="text-align:justify"><b>Abstract:</b><b><span style="font-size:12.0pt"><o:p></o:p></span></b></p><p class="MsoNormal">Graphical&nbsp;models&nbsp;are a powerful framework used to succinctly represent high-dimensional distributions, and are at the core of modern large-scale statistical inference. The graph underlying such a distribution specifies interactions between
 the variables, and explicitly captures the computational aspect inherent to statistical tasks. For unstructured settings such as those found in social networks, biology, and finance, a central problem is to determine a good&nbsp;model&nbsp;from observed data.&nbsp;<o:p></o:p></p><p class="MsoNormal"><o:p>&nbsp;</o:p></p><p class="MsoNormal">Learning&nbsp;graphical&nbsp;models&nbsp;from high-dimensional data is computationally challenging, and many different algorithms have been proposed over the years. Nevertheless, it is not clear what classes of&nbsp;models&nbsp;can be learned efficiently. In this
 talk we explore the problem from several angles and obtain a relatively unified view of what determines the computational complexity of&nbsp;learning&nbsp;graphical&nbsp;models.&nbsp;<o:p></o:p></p><p class="MsoNormal"><span style="font-size:10.0pt;font-family:&quot;Times New Roman&quot;,&quot;serif&quot;">&nbsp;</span></p><p class="MsoNormal"><br>
<b>Bio:<br>
</b>Guy&nbsp;Bresler&nbsp;is currently a postdoctoral associate at the Laboratory for Information and Decision Systems at MIT. His research interests are at the interface of computation, statistics, and information theory. He received his Ph.D. from the Dept. of Electrical
 Engineering and Computer Sciences at UC Berkeley, and his B.S. in electrical and computer engineering and M.S. in mathematics from the Univ. of Illinois at Urbana-Champaign. He has received several fellowships and awards including the NSF graduate research
 fellowship and a Vodafone Foundation fellowship.<span style="font-size:10.0pt">&nbsp;<o:p></o:p></span></p><p class="MsoNormal" style="text-align:justify"><b><i><span style="font-size:12.0pt">&nbsp;</span></i></b></p><p class="MsoNormal"><o:p>&nbsp;</o:p></p>
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