Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare
in a geometrically faithful way point clouds and more generally probability distributions. The …

Anomaly detection is an important application in large-scale industrial manufacturing.
Recent methods for this task have demonstrated excellent accuracy but come with a latency …

Plugin estimation of smooth optimal transport maps Page 1 The Annals of Statistics 2024, Vol.
52, No. 3, 966–998 https://doi.org/10.1214/24-AOS2379 © Institute of Mathematical Statistics …

The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between
two probability distributions given a prior stochastic evolution. As well as applications in the …

Optimal transport (OT) theory has been used in machine learning to study and characterize
maps that can push-forward efficiently a probability measure onto another. Recent works …

Optimal transport maps between two probability distributions $\mu $ and $\nu $ on $\R^ d $
have found extensive applications in both machine learning and statistics. In practice, these …

We consider the problem of estimating the optimal transport map between two probability
distributions, $ P $ and $ Q $ in $\mathbb {R}^ d $, on the basis of iid samples. All existing …

Statistical Optimal Transport arXiv:2407.18163v2 [math.ST] 7 Nov 2024 Page 1 Statistical
Optimal Transport Sinho Chewi Yale Jonathan Niles-Weed NYU Philippe Rigollet MIT …

We present a new behavioural distance over the state space of a Markov decision process,
and demonstrate the use of this distance as an effective means of shaping the learnt …

We prove a central limit theorem for the entropic transportation cost between subgaussian
probability measures, centered at the population cost. This is the first result which allows for …