[AI Seminar] AI Seminar sponsored by Apple -- Jianbo Ye -- September 26

[Adams Wei Yu]

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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