<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><head><meta http-equiv="Content-Type" content="text/html; charset=us-ascii"></head><div dir="auto" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><br class=""><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><br class=""><b class="" style="orphans: 2; widows: 2;"><font size="4" class=""><div class=""><b class=""><span class="" style="font-size: 12pt; font-family: "Times New Roman", serif;"></span></b></div><div class="">Adji Bousso Dieng</div></font></b><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div dir="auto" class="" style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;"><div class="" style="orphans: 2; widows: 2;"><div class="" style="margin: 0in 0in 0.0001pt;"><i class=""><font size="4" class="">Columbia University</font></i></div><div class="" style="margin: 0in 0in 0.0001pt;"><span class="" style="font-size: 14px;"><i class=""><p class="MsoNormal" align="center"><o:p class=""></o:p></p></i></span><div class=""><br class=""></div></div><div class="" style="margin: 0in 0in 0.0001pt;"><span class=""><b class=""><font class="" size="4">Tuesday, February 18 at 2:00 pm<br class="">Crerar 390</font></b><br class=""></span></div></div><div class=""><div class="" style="text-align: center;"><font size="4" class=""><font class=""><span class=""><b class="" style="color: rgb(33, 33, 33);"><br class=""></b></span></font></font></div><div class=""><font size="4" class=""><font class=""><span class=""><b class="" style="color: rgb(33, 33, 33);">Title: </b></span></font></font><font size="4" class=""><b class="">Deep Probabilistic Graphical Modeling</b></font></div><div class="" style="color: rgb(33, 33, 33);"><b class=""><font class="" style="font-size: 15px;"><br class=""></font></b></div><div class="" style="color: rgb(33, 33, 33);"><b class=""><font class="" size="4">Abstract:</font></b></div><div class=""><font class=""><span class=""><div class="" style="font-variant-ligatures: normal; background-color: rgb(255, 255, 255);"><div class=""><font class="" size="4"><span class=""><span id="docs-internal-guid-bf351367-7fff-5067-8c8e-0130cc54a81c" class=""><div class="" style="line-height: 1.38; margin-top: 0pt; margin-bottom: 0pt;"><span class=""><span class=""><span class=""><span class=""><span class=""><span style="white-space: pre-wrap;" class="">Deep learning (DL) is a powerful approach to modeling complex and large scale data. However, DL models lack interpretable quantities and calibrated uncertainty. In contrast, probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and a way to express uncertainty about what we do not know. How can we develop machine learning methods that bring together the expressivity of DL with the interpretability and calibration of PGM to build flexible models endowed with an interpretable latent structure that can be fit efficiently? I call this line of research deep probabilistic graphical modeling (DPGM). In this talk, I will discuss my work on developing DPGM for text data. In particular, I will show how DPGM enables flexible and interpretable topic modeling at large scale, unlocking several known challenges. Furthermore, I will describe how we can account for both local and long-range context, under the DPGM framework, to build a flexible sequential document model that leads to state-of-the-art performance on a downstream document classification task.</span><br class=""><br class=""></span></span></span></span></span></div></span></span><b class="" style="color: rgb(33, 33, 33);">Bio:</b></font></div></div></span></font></div><div class=""><div class="" style="font-variant-ligatures: normal; background-color: rgb(255, 255, 255);"><font color="#222222" class=""><font size="4" class=""><i class="">Adji Bousso Dieng is a PhD Candidate at Columbia University where she is jointly advised by David Blei and John Paisley. Her research is in Artificial Intelligence and Statistics, bridging probabilistic graphical models and deep learning. Dieng is supported by a Dean Fellowship from Columbia University. She won a Microsoft Azure Research Award and a Google PhD Fellowship in Machine Learning. She was recognized as a rising star in machine learning by the University of Maryland. </i></font></font></div><div class="" style="font-variant-ligatures: normal; background-color: rgb(255, 255, 255);"><font color="#222222" class=""><font size="4" class=""><i class=""><br class=""></i></font></font></div><div class="" style="font-variant-ligatures: normal; background-color: rgb(255, 255, 255);"><font color="#222222" class=""><font size="4" class=""><i class="">Prior to Columbia, Dieng worked as a Junior Professional Associate at the World Bank. She did her undergraduate studies in France where she attended Lycee Henri IV and Telecom ParisTech--France's Grandes Ecoles system. She spent the third year of Telecom ParisTech's curriculum at Cornell University where she earned a Master in Statistics.</i><br class=""><i class=""> </i></font><br class=""><i class=""><b class=""><font size="4" class="">Host: Michael Maire</font></b></i></font></div><div class="" style="font-variant-ligatures: normal; background-color: rgb(255, 255, 255);"><font color="#222222" class=""><i class=""><b class=""><font size="4" class=""><br class=""></font></b></i></font></div><div class="" style="font-variant-ligatures: normal; background-color: rgb(255, 255, 255);"><font color="#222222" class=""><i class=""><b class=""><font size="4" class="">PDF:</font></b></i></font></div><div class="" style="font-variant-ligatures: normal; background-color: rgb(255, 255, 255);"></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></body></html>