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Bayesian Learning via Neural Schr\"odinger-F\"ollmer Flows

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arxiv 2111.10510 v9 pith:OQA5JWQE submitted 2021-11-20 stat.ML cs.LG

Francisco Vargas , Andrius Ovsianas , David Fernandes , Mark Girolami , Neil D. Lawrence , Nikolas Nüsken This is my paper · ORCID
classification stat.MLcs.LG
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In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i.e. Schr\"odinger bridges). We advocate stochastic control as a finite time and low variance alternative to popular steady-state methods such as stochastic gradient Langevin dynamics (SGLD). Furthermore, we discuss and adapt the existing theoretical guarantees of this framework and establish connections to already existing VI routines in SDE-based models.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Path Integral Optimiser: Global Optimisation via Neural Schr\"odinger-F\"ollmer Diffusion

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A neural Schrödinger-Föllmer diffusion, trained like the Path Integral Sampler, is repurposed as a global optimizer, with new conditional convergence bounds and competitive results on small tasks only.

  2. No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers

    cs.LG 2025-02 conditional novelty 6.0 of 10

    Simulation-free training of neural samplers fails without Langevin preconditioning, and parallel tempering followed by fitting a diffusion model is a stronger baseline than most neural samplers.

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