Recent years have seen growing connections between generative modeling and broader problems in measure transport, sampling, inference, and control. These connections have led to new theoretical insights as well as practical advances in model training, fine-tuning, inference-time alignment, and scientific simulation and discovery. At the same time, many of the underlying challenges are shared across traditionally separate research communities.
The goal of the workshop is to foster dialogue across communities working at the intersection of generative modeling, sampling, stochastic control, scientific machine learning, and related areas. By bringing together perspectives from machine learning, statistics, applied mathematics, physics, chemistry, and computational biology, the workshop aims to highlight shared mathematical and algorithmic principles, explore foundational questions and emerging applications, and create opportunities for new collaborations. We hope it will serve as a forum for exchanging ideas, identifying promising directions, and accelerating progress on both fundamental and applied problems.
Confirmed speakers
Organizing committee
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