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arXiv Machine Learning By Sebastian Salazar, Dilum Fernando, Michal Kucer, Emily Casleton, Luhuan Wu, David M. Blei

WASABI: Wasserstein Sampling via Bridge Interpolants

Read the original on arXiv Machine Learning →

WASABI (Wasserstein Sampling via Bridge Interpolants) is a diffusion‑model based method for sampling from Boltzmann distributions using only the energy function. It trains the model by minimizing the KL divergence to the target distribution through Wasserstein gradient flows, avoiding the need to compute the model’s log‑density gradient. After training, WASABI can generate new samples without further energy or gradient evaluations, achieving superior performance on alanine dipeptide and competitive results on interacting particle benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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