Conditional Diffusion Sampling
Sampling from unnormalized multimodal distributions with limited density evaluations remains a fundamental challenge in machine learning and natural sciences. Successful approaches construct a bridge between a tractable reference and the target distribution. Parallel Tempering (PT) serves as the gold standard, while recent diffusion-based approaches offer a continuous alternative at the cost of neural training. In this work, we introduce Conditional Diffusion Sampling (CDS), a framework that combines these two paradigms. To this end, we derive Conditional Interpolants, a class of stochastic processes whose transport dynamics are governed by an exact, closed-form stochastic differential eq...
| Authors | Francisco M. Castro-Macías, Pablo Morales-Álvarez, Saifuddin Syed et al. |
|---|---|
| Primary category | stat.ML |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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