[AI Seminar] ai-seminar-announce Digest, Vol 76, Issue 6

[Adams Wei Yu]

A gentle reminder that the talk will happen tomorrow (Tuesday) noon in NSH
1507.

On Sun, Sep 24, 2017 at 9:00 AM, <ai-seminar-announce-request at cs.cmu.edu>
wrote:

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