<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;"> </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> </i><em><span style="font-family:"Calibri","sans-serif";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> </i><em><span style="font-family:"Calibri","sans-serif";mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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Learning statistical structure in data: efficient estimation of graphical 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> </i><em><span style="font-family:"Calibri","sans-serif";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:"Calibri","sans-serif";mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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<em><span style="font-family:"Calibri","sans-serif";mso-fareast-language:KO;font-style:normal"> </span></em></p>
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<em><b><span style="font-family:"Calibri","sans-serif";mso-fareast-language:KO;font-style:normal"> </span></b></em></p>
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<strong><i>Time:</i></strong><strong><i><span style="font-family:"Calibri","sans-serif";mso-fareast-language:KO"><o:p></o:p></span></i></strong></p>
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<em><span style="font-family:"Calibri","sans-serif";font-style:normal">4:30 pm</span></em><em><span style="font-family:"Calibri","sans-serif";mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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<strong><i>Room:</i></strong><i> </i><em><span style="font-family:"Calibri","sans-serif";mso-fareast-language:KO;font-style:normal"><o:p></o:p></span></em></p>
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<em><span style="font-family:"Calibri","sans-serif";font-style:normal">B205, E-Quad</span></em><em><span style="font-family:"Calibri","sans-serif";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:"Calibri","sans-serif";mso-fareast-language:KO"><o:p></o:p></span></i></strong></p>
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<em><span style="font-family:"Calibri","sans-serif";font-style:normal">Prof. Emmanuel Abbe</span></em><em><span style="font-family:"Calibri","sans-serif";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"> </span></p><p class="MsoNormal" style="text-align:justify"><b> </b></p><p class="MsoNormal" style="text-align:justify"><b> </b></p><p class="MsoNormal" style="text-align:justify"><b> </b></p><p class="MsoNormal" style="text-align:justify"><b> </b></p><p class="MsoNormal" style="text-align:justify"><b> </b></p><p class="MsoNormal" style="text-align:justify"><b> </b></p><p class="MsoNormal" style="text-align:justify"><b> </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 models 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 model from observed data. <o:p></o:p></p><p class="MsoNormal"><o:p> </o:p></p><p class="MsoNormal">Learning graphical models 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 models 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 learning graphical models. <o:p></o:p></p><p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Times New Roman","serif""> </span></p><p class="MsoNormal"><br>
<b>Bio:<br>
</b>Guy Bresler 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"> <o:p></o:p></span></p><p class="MsoNormal" style="text-align:justify"><b><i><span style="font-size:12.0pt"> </span></i></b></p><p class="MsoNormal"><o:p> </o:p></p>
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