<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><div><br class=""><blockquote type="cite" class=""><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px;" class=""><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">From: </b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">"Gina M. Holland" <<a href="mailto:gholland@princeton.edu" class="">gholland@Princeton.EDU</a>><br class=""></span></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px;" class=""><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">Subject: </b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class=""><b class="">NEXT: Joint PACM-CSML Colloquium: APR 30/David Blei (Rescheduled)</b><br class=""></span></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px;" class=""><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">Date: </b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">April 24, 2018 at 10:35:12 AM EDT<br class=""></span></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px;" class=""><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">To: </b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">"<a href="mailto:pacmdistribution@math.princeton.edu" class="">pacmdistribution@math.princeton.edu</a>" <<a href="mailto:pacmdistribution@math.princeton.edu" class="">pacmdistribution@math.princeton.edu</a>><br class=""></span></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px;" class=""><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif; color:rgba(0, 0, 0, 1.0);" class=""><b class="">Cc: </b></span><span style="font-family: -webkit-system-font, Helvetica Neue, Helvetica, sans-serif;" class="">"Andrea E. La Bella" <<a href="mailto:alabella@princeton.edu" class="">alabella@Princeton.EDU</a>><br class=""></span></div><br class=""><div class=""><div style="position: relative;" class=""><div class="WordSection1" style="page: WordSection1; font-family: Helvetica; font-size: 18px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-stroke-width: 0px;"><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="font-size: 12pt; font-family: 'Times New Roman', serif;" class="">The next colloquium will be a Joint PACM-CSML Colloquium given by David Blei, Columbia University</span><o:p class=""></o:p></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="font-size: 12pt; font-family: 'Times New Roman', serif;" class=""><br class="">Title and abstract are below.</span><span style="font-size: 8pt; font-family: 'Times New Roman', serif;" class=""> </span><o:p class=""></o:p></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="font-size: 12pt; font-family: 'Times New Roman', serif;" class=""><br class="">=================<br class="">DATE/LOCATION: Monday, April 30, 2018, 4:00 – 5:00 PM / 214 Fine Hall<br class=""><br class="">SPEAKER: David Blei, Columbia University<br class=""><br class="">TITLE: Scaling and Generalizing Bayesian Inference<b class=""><o:p class=""></o:p></b></span></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="font-size: 12pt; font-family: 'Times New Roman', serif;" class=""> </span><o:p class=""></o:p></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="font-size: 12pt; font-family: 'Times New Roman', serif;" class="">ABSTRACT:</span> <span class="Apple-converted-space"> </span><span style="font-size: 12pt; font-family: 'Times New Roman', serif;" class="">A core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in probabilistic modeling, which frames all inference about unknown quantities as a calculation about a conditional distribution. In this talk I review and discuss variational inference (VI), a method that approximates probability distributions through optimization. VI has been used in myriad applications in machine learning. It tends to be faster than more traditional methods, such as Markov chain Monte Carlo sampling.</span><o:p class=""></o:p></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><o:p class=""> </o:p></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="position: absolute; z-index: 251657726; margin-left: 10px; margin-top: 47px; width: 413px; height: 228px;" class=""><img class="" apple-inline="yes" id="4FC7AA28-EDCE-472F-8BF3-FD30515A31A4" height="228" width="413" apple-width="yes" apple-height="yes" src="cid:image002.png@01D3DBB7.EE20EE30"></span><span style="font-size: 12pt; font-family: 'Times New Roman', serif;" class="">I discuss our recent research on VI. I first describe stochastic variational inference, an approximate inference algorithm for handling massive data sets, and demonstrate its application to probabilistic topic models of millions of articles. Then I discuss black box variational inference, a generic algorithm for approximating the posterior. Black box inference easily applies to many models but requires minimal mathematical work to implement. I will demonstrate black box inference on deep exponential families---a method for Bayesian deep learning---and describe how it enables powerful tools for probabilistic programming.<o:p class=""></o:p></span></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><o:p class=""> </o:p></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="font-size: 12pt; font-family: 'Times New Roman', serif;" class="">=================</span><o:p class=""></o:p></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="color: rgb(31, 73, 125);" class=""> <o:p class=""></o:p></span></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="color: rgb(31, 73, 125);" class=""><img class="" apple-inline="yes" id="6B8903F6-3827-406C-90C6-92A1CBE70E9B" height="96" width="96" apple-width="yes" apple-height="yes" src="cid:image006.png@01D3DBB7.41076C60"></span><o:p class=""></o:p></div><div style="margin: 0in 0in 0.0001pt; font-size: 11pt; font-family: Calibri, sans-serif;" class=""><span style="color: rgb(31, 73, 125);" class=""><img class="" apple-inline="yes" id="ECE27AFD-32D8-4A47-9BCA-8752AA982224" height="48" width="128" apple-width="yes" apple-height="yes" src="cid:image007.png@01D3DBB7.41076C60"></span></div></div></div></div></blockquote></div><br class=""></body></html>