Scientific

Optimal transport (OT) has recently gained lot of interest in machine learning. It is a natural tool to compare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is however plagued by the curse of dimensionality, since it might require a number of samples which grows exponentially with the dimension. In this course, I will explain how to leverage entropic regularization methods to define computationally efficient loss functions, approximating OT with a better sample complexity. More information and references can be found on the website of our book “Computational Optimal Transport”.

• Lecture 1
• Lecture 2
• Lecture 3

In this lecture series we present an overview of dynamical optimal transport and some of its applications to discrete probability and non-commutative analysis. Particular focus is on gradient structures and functional inequalities for dissipative quantum systems, and on homogenisation results for dynamical optimal transport.

• Lecture 1
• Lecture 2
• Lecture 3

In this lecture series we present an overview of dynamical optimal transport and some of its applications to discrete probability and non-commutative analysis. Particular focus is on gradient structures and functional inequalities for dissipative quantum systems, and on homogenisation results for dynamical optimal transport.

• Lecture 1
• Lecture 2
• Lecture 3

Gross substitutes is a fundamental property in mathematics, economics and computation, almost as important as convexity. It is at the heart of optimal transport theory – although this is often underrecognized – and understanding the connection key to understanding the extension of optimal transport to other models of matching.

Lecture 1. Introduction to gross substitutes M-matrices and M-maps, nonlinear Perron-Froebenius theory, convergence of Jacobi algorithm. A toy hedonic model.

Lecture 2. Models of matching with transfers Problem formulation, regularized and unregularized case. IPFP and its convergence. Existence and uniqueness of an equilibrium. Lattice structure.

Lecture 3. Models of matching without transfers Gale and Shapley’s stable matchings. Adachi’s formulation. Kelso-Craford. Hatfield-Milgrom.

New measurement technologies like single-cell RNA sequencing are bringing ‘big data’ to biology. One of the most exciting prospects associated with this new trove of data is the possibility of studying temporal processes, such as differentiation and development. In this talk, we introduce the basic elements of a mathematical theory to answer questions like How does a stem cell transform into a muscle cell, a skin cell, or a neuron? How can we reprogram a skin cell into a neuron? We model a developing population of cells with a curve in the space of probability distributions on a high-dimensional gene expression space. We design algorithms to recover these curves from samples at various time-points and we collaborate closely with experimentalists to test these ideas on real data.