Functional tensor trains plus BSDE regression solve the HJB score PDE, yielding a fast low-rank sampler that outperforms neural diffusion methods on multimodal targets.
Latuszy ´nski, M
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We explain the fundamental challenges of sampling from multimodal distributions, particularly for high-dimensional problems. We present the major types of MCMC algorithms that are designed for this purpose, including parallel tempering, mode jumping and Wang-Landau, as well as several state-of-the-art approaches that have recently been proposed. We demonstrate these methods using both synthetic and real-world examples of multimodal distributions with discrete or continuous state spaces.
representative citing papers
The method couples Bayesian spectral deconvolution with a Gaussian process physical-property regression layer to select peak models consistent with auxiliary measurements, recovering meaningful structures missed by spectrum-only inference.
Energy-Weighted Flow Matching reformulates conditional flow matching with importance sampling to enable continuous normalizing flows to model Boltzmann distributions from energy evaluations alone, with iterative and annealed variants showing competitive performance on benchmarks.
citing papers explorer
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Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling
Functional tensor trains plus BSDE regression solve the HJB score PDE, yielding a fast low-rank sampler that outperforms neural diffusion methods on multimodal targets.
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Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak Estimation
The method couples Bayesian spectral deconvolution with a Gaussian process physical-property regression layer to select peak models consistent with auxiliary measurements, recovering meaningful structures missed by spectrum-only inference.
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Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling
Energy-Weighted Flow Matching reformulates conditional flow matching with importance sampling to enable continuous normalizing flows to model Boltzmann distributions from energy evaluations alone, with iterative and annealed variants showing competitive performance on benchmarks.