Tuesday, September 26, 2017. 12:00PM. NSH 1507.

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Jianbo Ye - 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).