AI Chat AI Image Generator AI Video Text to Speech

Optimal transport between determinantal point processes and application to fast simulation

11/02/2020

∙

by Laurent Decreusefond, et al.

∙

∙

We analyze several optimal transportation problems between de-terminantal point processes. We show how to estimate some of the distances between distributions of DPP they induce. We then apply these results to evaluate the accuracy of a new and fast DPP simulation algorithm. We can now simulate in a reasonable amount of time more than ten thousands points.

Laurent Decreusefond
12 publications
Guillaume Moroz
7 publications

page 1

page 2

page 3

page 4

research

∙ 06/04/2020

Fast Unbalanced Optimal Transport on Tree

This study examines the time complexities of the unbalanced optimal tran...

0 Ryoma Sato, et al. ∙

research

∙ 01/26/2023

On simulation of continuous determinantal point processes

We review how to simulate continuous determinantal point processes (DPPs...

0 Frédéric Lavancier, et al. ∙

research

∙ 10/01/2019

Training Generative Networks with general Optimal Transport distances

We propose a new algorithm that uses an auxiliary Neural Network to calc...

44 Vaios Laschos, et al. ∙

research

∙ 11/02/2022

Entropic Neural Optimal Transport via Diffusion Processes

We propose a novel neural algorithm for the fundamental problem of compu...

6 Nikita Gushchin, et al. ∙

research

∙ 06/12/2023

Extending JumpProcess.jl for fast point process simulation with time-varying intensities

Point processes model the occurrence of a countable number of random poi...

0 Guilherme Augusto Zagatti, et al. ∙

research

∙ 01/23/2018

Optimal Transport on Discrete Domains

Inspired by the matching of supply to demand in logistical problems, the...

0 Justin Solomon, et al. ∙

research

∙ 10/01/2019

Wasserstein Neural Processes

Neural Processes (NPs) are a class of models that learn a mapping from a...

0 Andrew Carr, et al. ∙