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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal style='background:white'><span style='color:black'><a href="https://statistics.yale.edu/" title="Department of Statistics and Data Science
"><span style='font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#286DC0;text-decoration:none'><img border=0 width=150 height=49 style='width:1.5625in;height:.5104in' id=logo src="cid:image001.jpg@01D93556.C626A020" alt="Department of Statistics and Data Science
"></span></a> <a href="https://statistics.yale.edu/" title=Home><b><span style='font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#286DC0'>Department of Statistics and Data Science </span></b></a></span><b><i><u><span style='font-size:22.0pt;font-family:"Lucida Sans",sans-serif;color:#286DC0'> <o:p></o:p></span></u></i></b></p><p class=MsoNormal><b><span style='font-size:14.0pt'>Moving forward our in-person seminars will be held at Mason</span></b><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'> Lab, room 211, 9 Hillhouse Avenue with the option of virtual participation<o:p></o:p></span></b></p><p class=MsoNormal><span class=MsoHyperlink><span style='color:#0070C0'>(</span></span><span class=MsoHyperlink><span style='font-size:12.0pt;font-family:"Arial",sans-serif;color:#0070C0;text-decoration:none'><a href="https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa"><span style='color:#0070C0;text-decoration:none'>https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa</span></a></span></span><span class=MsoHyperlink><span style='color:#0070C0;text-decoration:none'>)</span></span><span class=MsoHyperlink><span style='color:#0070C0'><o:p></o:p></span></span></p><p class=MsoNormal><a href="x-apple-data-detectors://10/"><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif;color:black;text-decoration:none'>3:</span></b><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif;color:windowtext;text-decoration:none'>30</span></b><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif;color:black;text-decoration:none'>pm</span></b></a><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif;color:black'> - Pre-talk meet and greet teatime - Dana House, 24 Hillhouse Ave. </span></b><b><span style='font-size:14.0pt'><o:p></o:p></span></b></p><h1 style='margin:0in'><span style='font-size:14.0pt;font-family:"Arial",sans-serif'><o:p> </o:p></span></h1><h1 style='margin:0in'><span style='font-size:14.0pt;font-family:"Arial",sans-serif'>Frederic Koehler, <span class=odd>Stanford University</span><o:p></o:p></span></h1><p class=MsoNormal><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f">
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</v:shape><![endif]--><![if !vml]><img width=150 height=180 style='width:1.5625in;height:1.875in' src="cid:image003.jpg@01D9360E.01B59620" align=left hspace=12 v:shapes="Picture_x0020_3"><![endif]><o:p></o:p></p><p class=MsoNormal><span class=date-display-single><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Date: Wednesday, February 01, 2023<o:p></o:p></span></span></p><p class=MsoNormal><span class=date-display-single><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Time: </span></span><span class=date-display-start><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>4:00PM</span></span><span class=date-display-range><span style='font-size:12.0pt;font-family:"Arial",sans-serif'> to </span></span><span class=date-display-end><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>5:00PM</span></span><o:p></o:p></p><p class=MsoNormal><span class=fn><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Location: Mason Lab 211</span></span><span style='font-size:12.0pt;font-family:"Arial",sans-serif'> <span class=map-icon><span style='letter-spacing:.6pt'><a href="http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us"><span style='color:windowtext'>see map</span></a> </span></span><o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>9 Hillhouse Ave<o:p></o:p></span></p><p class=MsoNormal><span class=locality><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>New Haven</span></span><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>, <span class=region>CT</span> <span class=postal-code>06511</span><o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><a href="https://frkoehle.github.io/"><span style='color:#003C76'>Website</span></a><o:p></o:p></span></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><o:p> </o:p></span></b></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><o:p> </o:p></span></b></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><o:p> </o:p></span></b></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Title: Towards the Statistically Principled Design of ML Algorithms<o:p></o:p></span></b></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><o:p> </o:p></span></b></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Information and Abstract: <o:p></o:p></span></b></p><p style='margin:0in;box-sizing: inherit'><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>What are the optimal algorithms for learning from data? Have we found them already, or are better ones out there to be discovered? Making these questions precise, and answering them, requires taking on the mathematically deep interplay between statistical and computational considerations. It also requires reconciling our theoretical toolbox with surprising new phenomena arising from practice, which seem to violate conventional rules of thumb regarding algorithm and model design. I will discuss progress along these lines: in terms of designing new algorithms for basic learning problems, controlling generalization in large statistical models, and understanding statistical questions arising from generative modeling.<o:p></o:p></span></p><p style='margin:0in;box-sizing: inherit'><strong><i><span style='font-family:"Calibri",sans-serif'><o:p> </o:p></span></i></strong></p><p style='margin:0in'><strong><i><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with the option of virtual participation </span></i></strong><strong><i><span style='font-family:"Calibri",sans-serif'>(</span></i></strong><a href="https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa"><span style='color:#286DC0'>https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa</span></a>)<o:p></o:p></p><p style='margin:0in;box-sizing: inherit'><strong><span style='font-size:12.0pt;font-family:"Arial",sans-serif;font-weight:normal'><o:p> </o:p></span></strong></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>For more details and upcoming events visit our website at <a href="http://statistics.yale.edu/"><span style='color:windowtext;text-decoration:none'>http://statistics.yale.edu/</span></a></span><o:p></o:p></p><p style='margin:0in'><o:p> </o:p></p><h2 style='margin:0in;box-sizing: inherit;font-feature-settings: "kern", "liga", "dlig"'><span style='font-family:"Georgia",serif;font-weight:normal'>Department of Statistics and Data Science<o:p></o:p></span></h2><p style='margin:0in;box-sizing: inherit'><span style='font-size:9.0pt;color:black'>Yale University<br>24 Hillhouse Avenue<br>New Haven, CT 06511<o:p></o:p></span></p><p style='margin:0in;box-sizing: inherit'><span style='font-size:9.0pt;color:black'>t 203.432.0666<br>f 203.432.0633<o:p></o:p></span></p><p class=MsoNormal style='line-height:18.0pt;background:white'><o:p> </o:p></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>