Computational optimal transport: With applications to data science
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 …
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …
Maximum flow and minimum-cost flow in almost-linear time
We give an algorithm that computes exact maximum flows and minimum-cost flows on
directed graphs with m edges and polynomially bounded integral demands, costs, and …
directed graphs with m edges and polynomially bounded integral demands, costs, and …
Distribution matching for crowd counting
In crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …
Optimal transport for treatment effect estimation
Estimating individual treatment effects from observational data is challenging due to
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …
treatment selection bias. Prevalent methods mainly mitigate this issue by aligning different …
Diffusion bridge mixture transports, Schrödinger bridge problems and generative modeling
S Peluchetti - Journal of Machine Learning Research, 2023 - jmlr.org
The dynamic Schrödinger bridge problem seeks a stochastic process that defines a
transport between two target probability measures, while optimally satisfying the criteria of …
transport between two target probability measures, while optimally satisfying the criteria of …
Faster Wasserstein distance estimation with the Sinkhorn divergence
The squared Wasserstein distance is a natural quantity to compare probability distributions
in a non-parametric setting. This quantity is usually estimated with the plug-in estimator …
in a non-parametric setting. This quantity is usually estimated with the plug-in estimator …
Projection‐based techniques for high‐dimensional optimal transport problems
Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …
measures, such that the transformation has the minimum transportation cost. Such a …
On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms
We provide theoretical analyses for two algorithms that solve the regularized optimal
transport (OT) problem between two discrete probability measures with at most $ n $ atoms …
transport (OT) problem between two discrete probability measures with at most $ n $ atoms …
Unified mask embedding and correspondence learning for self-supervised video segmentation
The objective of this paper is self-supervised learning of video object segmentation. We
develop a unified framework which simultaneously models cross-frame dense …
develop a unified framework which simultaneously models cross-frame dense …
[PDF][PDF] Statistical optimal transport
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 …
Optimal Transport Sinho Chewi Yale Jonathan Niles-Weed NYU Philippe Rigollet MIT …