Optimal transport has recently been reintroduced to the machine learning community thanks in part to novel efficient optimization procedures allowing for medium to large scale …
Computing optimal transport distances such as the earth mover's distance is a fundamental problem in machine learning, statistics, and computer vision. Despite the recent introduction …
J Zhang, P Ma, W Zhong, C Meng - Wiley Interdisciplinary …, 2023 - Wiley Online Library
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 …
We consider the task of aligning two sets of points in high dimension, which has many applications in natural language processing and computer vision. As an example, it was …
Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models. Resampling is a key ingredient of PF necessary …
Sorting is used pervasively in machine learning, either to define elementary algorithms, such as $ k $-nearest neighbors ($ k $-NN) rules, or to define test-time metrics, such as top-$ k …
FP Paty, M Cuturi - International conference on machine …, 2019 - proceedings.mlr.press
Making sense of Wasserstein distances between discrete measures in high-dimensional settings remains a challenge. Recent work has advocated a two-step approach to improve …
Abstract Linear Discriminant Analysis (LDA) is a popular technique for supervised dimensionality reduction, and its performance is satisfying when dealing with Gaussian …
Estimation of Wasserstein distances in the Spiked Transport Model Page 1 Bernoulli 28(4), 2022, 2663–2688 https://doi.org/10.3150/21-BEJ1433 Estimation of Wasserstein distances …