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<!DOCTYPE HTML>
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<html>
<head>
<title>DeeplyBayesian 2019</title>
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<!-- Wrapper -->
<div id="wrapper">
<!-- Header -->
<header id="header" class="alt">
<!-- <span class="logo"><img src="images/logo_mit_ibm.png" alt="" /></span> -->
<h1>First workshop on Practical Bayesian Methods for Big Data </h1>
<p> Location: MIT Samberg Center<br /> (<a href="#dir">Getting Here</a>)</p>
<p> September 20, 2019 </p>
</header>
<!-- Nav -->
<nav id="nav">
<ul>
<li><a href="#intro" class="active">Introduction</a></li>
<li>Registration</li>
<li><a href="#cfp">Call For Participation</a></li>
<li><a href="#sched">Schedule</a></li>
<li><a href="#inv">Invited Speakers</a></li>
<!-- <li><a href="#panel">Featured Panelists</a></li> -->
<li><a href="#org">Organizers</a></li>
<li><a href="https://ibm.biz/ai-research-week">IBM Research AI Week</a></li>
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<header class="major">
<h2>About</h2>
</header>
<!-- <p style="text-align:center"><img src="images/banner_1.jpg" alt="MIT Watson AI Lab, Cambridge, MA"
width="1024" height="256">
</p> -->
<p style="text-align:justify"> On Friday September 20th, 2019 as part of <a href="https://ibm.biz/ai-research-week">IBM Research's
AI week</a> we will be hosting the first workshop on Practical Bayesian methods for Big Data.
</p>
<p style="text-align:justify"> Bayesian methods have long benefited from their ability to both coherently represent uncertainty and incorporate prior knowledge, but have traditionally struggled to scale to both large data and large models. Deep learning approaches empirically demonstrate the benefits of learning large over-parameterized models from large data, but struggle with producing well calibrated uncertainties. Research attempting to both scale up Bayesian methods and combine the benefits of either paradigm has recently garnered significant attention. Examples include deep generative models and Bayesian neural networks. This workshop will advance and accelerate research on statistical underpinnings of methods at this intersection, including recent advances in Bayesian approaches for learning neural network based models , deep learning methods for Bayesian modeling, methods for scaling up Bayesian inference to large models and data, and use of classical statistical tools for measuring robustness and reliability of deep learning models.
</p>
</div>
</section>
<!-- First Section -->
<section id="cfp" class="main special">
<header class="major">
<h2>Call For Participation</h2>
</header>
<p style="text-align:justify">
We invite researchers to submit work in (but not limited to) the following areas:
<ul style="text-align:left">
<li> Bayesian approaches for learning neural network based models. </li>
<li> Advances in deep generative modeling. </li>
<li> Deep learning methods for Bayesian modeling. </li>
<li> Methods for scaling up Bayesian inference to large models and data. </li>
<li> Methods for measuring robustness and reliability of statistical models. </li>
</ul>
</P>
<h2 style="text-align:left">Submissions</h2>
<p style="text-align:justify">
Submission can be made via an
<a href=https://easychair.org/conferences/>
EasyChair submission.</a>
The submission should be in the form of an extended abstract and should not exceed 3 pages (excluding references) in PDF format using NeurIPS style. Submissions of new ideas, recently published works and/or extension of existing works are welcome. Parallel submissions or submissions of under-review works are also permitted. Author names do not need to be anonymized.
Submission will be accepted as contributed 15-minute talks or poster presentations. The final versions will be posted on the workshop website (and are archival but do not constitute a proceeding).
</p>
<h2 style="text-align:left"> Key Dates </h2>
<ul style="text-align:left">
<li>Abstracts due: Abstract submission is now closed. </li>
<li>Notification to Submitters: 03/26/2018. </li>
<li> Meeting Date: 04/27/2018 </li>
</ul>
<h2 style="text-align:left">Attendance</h2>
<p style="text-align:justify"> For each accepted paper or poster,
at least one author must attend the workshop and present the
paper/poster.
<br>
</section>
<!-- Agenda -->
<section id="sched" class="main special">
<header class="major">
<h2>Schedule (To be Finalized!)</h2>
</header>
<table style="width:100%">
<tr>
<td>8:30-9:00 AM</td> <td>Welcome and Opening Remarks </br> :
TBD</td>
</tr>
<tr>
<td>9:00-10:00 AM</td> <td> TBD </br>
</br>
</td>
</tr>
<tr><td>10:00-10:20 AM</td> <td>Coffee Break</td>
</tr>
<tr>
<td> 10:20-12:00 PM </td> <td> </br>
</td>
</tr>
</table>
</section>
<!-- Invited Speakers -->
<section id="inv" class="main special">
<header class="major">
<h2>Invited Speakers</h2>
</header>
<ul class="features">
<li>
<span><img src="images/" height="192"/></span>
<h3> Tamara Broderick</h3>
<p> Associate Professor, EECS, </br>
MIT </p>
</li>
<li>
<span><img src="images/" height="192"/></span>
<h3><a href=https://finale.seas.harvard.edu>Finale Doshi-Velez</a></h3>
<p> Assistant Professor, Computer Science, </br>
Harvard University</p>
</li>
<li>
<span><img src="images/" height="192"/></span>
<h3><a href="">Natesh Pillai</a></h3>
<p>Professor, Statistics, Harvard University</p>
</li>
<li>
<span><img src="images/" height="192"/></span>
<h3><a href="">Jan-Willem van de Meent</a></h3>
<p>Assistant Professor, College of Computer and Information Science,</br>
Northeastern</p>
</li>
</ul>
</section>
<!-- Organizers -->
<section id="org" class="main special">
<h2>Organizing Committee</h2>
<table style="float:center">
<tr>
<td><img src="images/" height="192"/></td>
<td><img src="images/" height="192"/></td>
<td><img src="images/" height="192"/></td>
</tr>
<tr>
<td><a href="">Nghia Hoang</a> </td>
<td><a href="">Mikhail Yurokchin</td>
<td><a href="">Kristen Severson</a></td>
</tr>
<tr>
<td><img src="images/" height="192"/></td>
<td><img src="images/" height="192"/></td>
<td><img src="images/" height="192"/></td>
</tr>
<tr>
<td><a href="">Prasanna Sattigeri</a></td>
<td><a href="">Akash Srivastava</a></td>
<td><a href="">Soumya Ghosh</a></td>
</tr>
</table>
</section>
<!-- Spotlights -->
<section id="spot" class="main special">
<header class="major">
<h2>Posters</h2>
</header>
<table style="width:100%">
</table>
</section>
</div>
<!-- Footer -->
<footer id="footer">
<section id="dir">
<h2>Location</h2>
<p style="text-align:justify">75 Binney St, Cambridge, MA 02142</br>
<i>Samberg center is located in Kendall square and easily accessible by public transportaion.
It is a short walk from the <a href=https://www.google.com/maps/dir/Kendall,+Cambridge,+MA+02142/MIT+Samberg+Conference+Center,+Memorial+Drive,+Cambridge,+MA/@42.3614109,-71.0867415,17z/data=!3m1!4b1!4m14!4m13!1m5!1m1!1s0x89e370af454cadd1:0xed3f24407286838c!2m2!1d-71.0855752!2d42.3624823!1m5!1m1!1s0x89e370a679984489:0x5c8d65db5c0d7efe!2m2!1d-71.0836961!2d42.360732!3e2>Kendall/MIT</a> stop on the red line and from <a href="https://www.google.com/maps/dir/Lechmere,+Cambridge,+MA/MIT+Samberg+Conference+Center,+Memorial+Drive,+Cambridge,+MA/@42.3652177,-71.0851221,16z/data=!3m1!4b1!4m14!4m13!1m5!1m1!1s0x89e370be837d0fa7:0x1c5777c77406d5f9!2m2!1d-71.077113!2d42.370088!1m5!1m1!1s0x89e370a679984489:0x5c8d65db5c0d7efe!2m2!1d-71.0836961!2d42.360732!3e2">Lechmere</a> on the green line.
We highly encourage using public transportation to get here. </i></p>
</section>
<section>
<h2>Contact</h2>
<dd>TBD </dd>
<dd>TBD </dd>
<dd>TBD </dd>
<dd>TBD </dd>
</section>
<p class="copyright">© Workshop Organizing Committee. Design: <a href="https://html5up.net">HTML5 UP</a>.</p>
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