Optimal transport (OT) theory can be informally described using the words of the French mathematician Gaspard Monge (1746-1818): A worker with a shovel in hand has to move a large pile of sand lying on a construction site. The goal of the worker is to erect with all that sand a target pile with a prescribed shape (for example, that of a giant sand castle). Naturally, the worker wishes to minimize her total effort, quantified for instance as the total distance or time spent carrying shovelfuls of sand. Mathematicians interested in OT cast that problem as that of comparing two probability distributions, two different piles of sand of the same volume. They consider all of the many possible ways to morph, transport or reshape the first pile into the second, and associate a "global" cost to every such transport, using the "local" consideration of how much it costs to move a grain of sand from one place to another. Recent years have witnessed the spread of OT in several fields, thanks to the emergence of approximate solvers that can scale to sizes and dimensions that are relevant to data sciences. Thanks to this newfound scalability, OT is being increasingly used to unlock various problems in imaging sciences (such as color or texture processing), computer vision and graphics (for shape manipulation) or machine learning (for regression, classification and density fitting). This short book reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications.

Computational Optimal Transport

Let $$\mu _N$$
 be the empirical measure associated to a $$N$$
 -sample of a given probability distribution $$\mu $$
 on $$\mathbb {R}^d$$
 . We are interested in the rate of convergence of $$\mu _N$$
 to $$\mu $$
 , when measured in the Wasserstein distance of order $$p>0$$
 . We provide some satisfying non-asymptotic $$L^p$$
 -bounds and concentration inequalities, for any values of $$p>0$$
 and $$d\ge 1$$
 . We extend also the non asymptotic $$L^p$$
 -bounds to stationary $$\rho $$
 -mixing sequences, Markov chains, and to some interacting particle systems.

https://hal.archives-ouvertes.fr/hal-00915365/document

On the rate of convergence in Wasserstein distance of the empirical measure

Let $\mu_N$ be the empirical measure associated to a $N$-sample of a given probability distribution $\mu$ on $\mathbb{R}^d$. We are interested in the rate of convergence of $\mu_N$ to $\mu$, when measured in the Wasserstein distance of order $p>0$. We provide some satisfying non-asymptotic $L^p$-bounds and concentration inequalities, for any values of $p>0$ and $d\geq 1$. We extend also the non asymptotic $L^p$-bounds to stationary $\rho$-mixing sequences, Markov chains, and to some interacting particle systems.

The Wasserstein distance between two probability measures on a metric space
is a measure of closeness with applications in statistics, probability, and
machine learning. In this work, we consider the fundamental question of how
quickly the empirical measure obtained from $n$ independent samples from $\mu$
approaches $\mu$ in the Wasserstein distance of any order. We prove sharp
asymptotic and finite-sample results for this rate of convergence for general
measures on general compact metric spaces. Our finite-sample results show the
existence of multi-scale behavior, where measures can exhibit radically
different rates of convergence as $n$ grows.

Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover the other distribution. They are ubiquitous in mathematics, with a long history that has seen them catalyse core developments in analysis, optimization, and probability. Beyond their intrinsic mathematical richness, they possess attractive features that make them a versatile tool for the statistician: they can be used to derive weak convergence and convergence of moments, and can be easily bounded; they are well-adapted to quantify a natural notion of perturbation of a probability distribution; and they seamlessly incorporate the geometry of the domain of the distributions in question, thus being useful for contrasting complex objects. Consequently, they frequently appear in the development of statistical theory and inferential methodology, and have recently become an object of inference in themselves. In this review, we provide a snapshot of the main concepts involved in Wasserstein distances and optimal transportation, and a succinct overview of some of their many statistical aspects.

Statistical Aspects of Wasserstein Distances

In this work, we provide non-asymptotic bounds for the average speed of convergence of the empirical measure in the law of large numbers, in Wasserstein distance. We also consider occupation measures of ergodic Markov chains. One motivation is the approximation of a probability measure by finitely supported measures (the quantization problem). It is found that rates for empirical or occupation measures match or are close to previously known optimal quantization rates in several cases. This is notably highlighted in the example of infinite-dimensional Gaussian measures.

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On the mean speed of convergence of empirical and occupation measures in Wasserstein distance

In this paper we tackle the problem of comparing distributions of random variables and defining a mean pattern between a sample of random events. Using barycenters of measures in the Wasserstein space, we propose an iterative version as an estimation of the mean distribution. Moreover, when the distributions are a common measure warped by a centered random operator, then the barycenter enables to recover this distribution template.

https://hal.archives-ouvertes.fr/hal-01291302/document

Distribution's template estimate with Wasserstein metrics

Séminaire de probabilités XLIX

We consider a random walk Zn1,',ZnK+1∈i¾?K+1 with the constraint that each coordinate of the walk is at distance one from the following one. In this paper, we show that this random walk is slowed down by a variance factor i¾?K2=2K+2 with respect to the case of the classical simple random walk without constraint. © 2014 Wiley Periodicals, Inc. Random Struct. Alg., 47, 267-283, 2015

https://hal.archives-ouvertes.fr/hal-00742938/document

Emmanuel Boissard

Papers

On the mean speed of convergence of empirical and occupation measures in Wasserstein distance

Distribution's template estimate with Wasserstein metrics

Distribution's template estimate with Wasserstein metrics

Séminaire de probabilités XLIX

Diffusivity of a random walk on random walks