Related papers: Solving Schr\"odinger Bridges via Maximum Likelihood

URL: http://arxiv.org/abs/2106.02081v1
Date: Thu, 3 Jun 2021 18:58:12 GMT
Title: Solving Schr\"odinger Bridges via Maximum Likelihood
Authors: Francisco Vargas, Pierre Thodoroff, Neil D. Lawrence, Austen Lamacraft
Abstract summary: The Schr"odinger bridge problem (SBP) finds the most likely evolution between two probability distributions. We prove an equivalence between the SBP and maximum likelihood estimation enabling direct application of successful machine learning techniques.
Score: 10.941628374136519
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The Schr\"odinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in the natural sciences, problems of this kind have important applications in machine learning such as dataset alignment and hypothesis testing. Whilst the theory behind this problem is relatively mature, scalable numerical recipes to estimate the Schr\"odinger bridge remain an active area of research. We prove an equivalence between the SBP and maximum likelihood estimation enabling direct application of successful machine learning techniques. We propose a numerical procedure to estimate SBPs using Gaussian process and demonstrate the practical usage of our approach in numerical simulations and experiments.

Err

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.