Newsroom
Sampling
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Yale’s Sinho Chewi Shares ICML 2026 Outstanding Paper Award
A paper co-authored by Sinho Chewi, “High-Accuracy Sampling for Diffusion Models and Log-Concave Distributions,” written with Fan Chen, Constantinos Daskalakis, and Alexander Rakhlin, received an ICML 2026 Outstanding Paper Award, one of only two papers to earn the top honor at this year’s International Conference on Machine Learning. Chewi is an Assistant Professor of Statistics…
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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.