Topics on Optimal Transport in Machine Learning and Shape Analysis

Course Info

Instructor Facundo Mémoli, m e m o l i @ m a t h . o s u . e d u
Course code CSE 5339 -- Spring 2018
Times: Thursdays 1.50 -- 3.40 pm
Location: BH 422.
Description: Optimal transport problems arisen the work of the french mathematician Gaspard Monge in the late 1700s. The original formulation was meant to model the optimal way of moving goods from a collection of sources to a collection of distribution points. The formulation was subsequently improved by the economist Leonid Kantorovich in the mid 1900s who found a more efficient linear programming relaxation.

In recent years ideas from optimal transport have been applied to problems in machine learning and geometric shape processing. This course will first cover the main ideas/concepts related to optimal transport, and will then explore several of its modern applications to ML and SA. Prerequisites: familiarity with discrete math/structures and interest in/familiarity with basic machine learning and shape analysis concepts.

Several possible research directions will be discussed.

Prerequisites: The course has minimal requisites: it is designed for students from Computer Science and Engineering, and Mathematics having knowledge of undergrad level math. Some knowledge of geometry will be useful, but not necessary. The course will provide the opportunity to explore different aspects of the material: interested students will have the opportunity of implementing some algorithms and/or exploring some research papers on different aspects of both the underlying mathematics and/or the algorithmic procedures.


Meeting 1 (1/11). First meeting.
Meeting 2 (1/18).
Meeting 3 (1/25).
Meeting 4 (2/1).
Meeting 5 (2/8).
Meeting 6 (2/15).
Meeting 7 (2/22).
Meeting 8 (3/1).
Meeting 9 (3/8). 9