Newsroom
Sampling
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S&DS Colloquium: Thuy-Duong “June” Vuong (Miller Institute, Berkeley), “Efficiently learning and sampling from multimodal distributions using data-based initialization”
Abstract: Learning to sample is a central task in generative AI: the goal is to generate (infinitely many more) samples from a target distribution $\mu$ given a small number of samples from $\mu.$ It is well-known that traditional algorithms such as Glauber or Langevin dynamics are highly inefficient when the target distribution is multimodal, as they…
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FDS x Applied Physics Colloquium: Grant Rotskoff (Stanford), “Efficient variational inference with generative models”
Abstract: Neural networks continue to surprise us with their remarkable capabilities for high-dimensional function approximation. Applications of machine learning now pervade essentially every scientific discipline, but predictive models to describe the optimization dynamics, inference properties, and flexibility of modern neural networks remain limited. In this talk, I will introduce several approaches to both analyzing and…
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FDS Conference: Recent Advances and Future Directions for Sampling
Hosted by The Yale Institute for Foundations of Data Science. Please join our mailing list for future announcements.