<div dir="ltr">A gentle reminder that the talk will happen tomorrow (Tuesday) noon in NSH 1507.</div><div class="gmail_extra"><br><div class="gmail_quote">On Sun, Sep 24, 2017 at 9:00 AM,  <span dir="ltr"><<a href="mailto:ai-seminar-announce-request@cs.cmu.edu" target="_blank">ai-seminar-announce-request@cs.cmu.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Send ai-seminar-announce mailing list submissions to<br>
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Today's Topics:<br>
<br>
   1.  AI Seminar sponsored by Apple -- Jianbo Ye --    September 26<br>
      (Adams Wei Yu)<br>
<br>
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Message: 1<br>
Date: Sun, 24 Sep 2017 02:12:51 -0700<br>
From: Adams Wei Yu <<a href="mailto:weiyu@cs.cmu.edu">weiyu@cs.cmu.edu</a>><br>
To: <a href="mailto:ai-seminar-announce@cs.cmu.edu">ai-seminar-announce@cs.cmu.edu</a><br>
Cc: <a href="mailto:jxy198@ist.psu.edu">jxy198@ist.psu.edu</a><br>
Subject: [AI Seminar] AI Seminar sponsored by Apple -- Jianbo Ye --<br>
        September 26<br>
Message-ID:<br>
        <CABzq7epRBO=<a href="mailto:7bbZgQa%2B87Gh26z63BYbXE9HjjC30cOrxSSDpOw@mail.gmail.com">7bbZgQa+<wbr>87Gh26z63BYbXE9HjjC30cOrxSSDpO<wbr>w@mail.gmail.com</a>><br>
Content-Type: text/plain; charset="utf-8"<br>
<br>
Dear faculty and students,<br>
<br>
We look forward to seeing you next Tuesday, September 26, at noon in NSH<br>
1507 (Unusual place) for AI Seminar sponsored by Apple. To learn more about<br>
the seminar series, please visit the AI Seminar webpage<br>
<<a href="http://www.cs.cmu.edu/~aiseminar/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>aiseminar/</a>>.<br>
<br>
On Tuesday, Jianbo Ye <<a href="http://personal.psu.edu/jxy198/" rel="noreferrer" target="_blank">http://personal.psu.edu/<wbr>jxy198/</a>> from PSU will give<br>
the following talk:<br>
<br>
Title: Optimal Transport for Machine Learning: The State-of-the-art<br>
Numerical Tools<br>
<br>
Abstract: Representation of datasets, classification and measurement of<br>
similarities/disparities between complex data or objects such as images or<br>
collection of histograms are ubiquitous problems in machine learning.<br>
Optimal transport based distances are used more and more frequently to<br>
address these questions. Despite its attractiveness, the calculations<br>
related to OT are quite non-trivial, posing great computational challenges<br>
to machine learning practitioners. In this talk, I will cover three major<br>
approaches including entropic regularization, Bregman ADMM and Gibbs<br>
sampling for approximately solving OT and variational Wasserstein problems<br>
in machine learning. Part of the talk is based on my joint work with Prof.<br>
James Z. Wang and Prof. Jia Li.<br>
<br>
Bio: Jianbo Ye is now a Ph.D. candidate at College of Information Science<br>
and Technology, The Pennsylvania State University. He works on machine<br>
learning, optimization methods and computational statistics with an<br>
emphasis on their connections to real-world. His thesis has been focused on<br>
developing scalable and robust numerical algorithms that apply optimal<br>
transport theory and Wasserstein geometry to machine learning models. He<br>
received the B.Sc. degree in Mathematics from University of Science and<br>
Technology of China (USTC). He has worked as a research intern at Intel<br>
(2013) and Adobe (2017).<br>
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End of ai-seminar-announce Digest, Vol 76, Issue 6<br>
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