Solving Schrödinger Bridges via Maximum Likelihood.
MetadataShow full item record
Vargas, F., Thodoroff, P., Lamacraft, A., & Lawrence, N. (2021). Solving Schrödinger Bridges via Maximum Likelihood.. https://doi.org/10.3390/e23091134
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 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 Schrödinger bridge remain an active area of research. Our main contribution is the proof of equivalence between solving the SBP and an autoregressive maximum likelihood estimation objective. This formulation circumvents many of the challenges of density estimation and enables 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.
Machine Learning, Stochastic Control, Schrödinger Bridges
Engineering and Physical Sciences Research Council (EP/T517847/1)
huawei technology co (NA)
External DOI: https://doi.org/10.3390/e23091134
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330088
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/