2 papers
cs.LG2026
Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
Aaron Havens, Brian Karrer, Neta Shaul
Sampling from unnormalized densities is analogous to the generative modeling problem, but the target distribution is defined by a known energy function instead of data samples. Bec…
cs.LG2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron Havens, Benjamin Kurt Miller, Bing Yan +10
We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the fi…