Bayesian learning via neural Schrödinger–Föllmer flows

Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken

Research output: Contribution to journalArticlepeer-review

Abstract

In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control. We advocate stochastic control as a finite time and low variance alternative to popular steady-state methods such as stochastic gradient Langevin dynamics. Furthermore, we discuss and adapt the existing theoretical guarantees of this framework and establish connections to already existing VI routines in SDE-based models.

Original languageEnglish
Article number3
JournalStatistics and Computing
Volume33
Issue number1
Early online date23 Nov 2022
DOIs
Publication statusE-pub ahead of print - 23 Nov 2022

Keywords

  • Bayesian deep learning
  • Bayesian inference
  • Föllmer drift
  • Schrödinger bridge problem
  • Stochastic control

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

Fingerprint

Dive into the research topics of 'Bayesian learning via neural Schrödinger–Föllmer flows'. Together they form a unique fingerprint.

Cite this