Accelerated Parallel Tempering via Neural Transports

• URL: http://arxiv.org/abs/2502.10328v2
• Date: Tue, 27 May 2025 14:46:51 GMT
• Title: Accelerated Parallel Tempering via Neural Transports
• Authors: Leo Zhang, Peter Potaptchik, Jiajun He, Yuanqi Du, Arnaud Doucet, Francisco Vargas, Hai-Dang Dau, Saifuddin Syed,
• Abstract summary: Parallel Tempering (PT) enhances MCMC's sample efficiency through parallel computation.<n>We introduce a framework that accelerates PT by leveraging neural samplers.<n>We demonstrate theoretically and empirically on a variety of multimodal sampling problems that our method improves sample quality.
• Score: 31.81728174953862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Markov Chain Monte Carlo (MCMC) algorithms are essential tools in computational statistics for sampling from unnormalised probability distributions, but can be fragile when targeting high-dimensional, multimodal, or complex target distributions. Parallel Tempering (PT) enhances MCMC's sample efficiency through annealing and parallel computation, propagating samples from tractable reference distributions to intractable targets via state swapping across interpolating distributions. The effectiveness of PT is limited by the often minimal overlap between adjacent distributions in challenging problems, which requires increasing the computational resources to compensate. We introduce a framework that accelerates PT by leveraging neural samplers-including normalising flows, diffusion models, and controlled diffusions-to reduce the required overlap. Our approach utilises neural samplers in parallel, circumventing the computational burden of neural samplers while preserving the asymptotic consistency of classical PT. We demonstrate theoretically and empirically on a variety of multimodal sampling problems that our method improves sample quality, reduces the computational cost compared to classical PT, and enables efficient free energies/normalising constants estimation.

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