TODAY: Rational Intelligence Seminar Series: Yixin Wang (University of Michigan)
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Krikamol Muandet
Jul 30, 2025, 7:23:14 AM7/30/25
to Machine Learning News
Hi everyone,
We’re thrilled to announce the upcoming seminar at the Rational Intelligence Seminar Series (RISS), on July 30, 2025 (TODAY)! RISS will bring you cutting-edge talks exploring the crucial connections between AI, decision-making, and causality. Our mission with this series is to deepen our understanding of what makes machine learning systems rational, efficient, and reliable.
Join us to engage in lively discussions in the session, “Posterior Mean Matching: Generative Modeling through Online Bayesian Inference,” delivered by Yixin Wang, Assistant Professor at the University of Michigan.
Abstract
This talk will introduce posterior mean matching (PMM), a new method for generative modeling that is grounded in Bayesian inference. PMM uses conjugate pairs of distributions to model complex data of various modalities like images and text, offering a flexible alternative to existing methods like diffusion models. PMM models iteratively refine noisy approximations of the target distribution using updates from online Bayesian inference. PMM is flexible because its mechanics are based on general Bayesian models. We demonstrate this flexibility by developing specialized examples: a generative PMM model of real-valued data using the Normal-Normal model, a generative PMM model of count data using a Gamma-Poisson model, and a generative PMM model of discrete data using a Dirichlet-Categorical model. Empirically, PMMs achieve performance that is competitive with generative models for language modeling and image generation.
We’re thrilled to announce the upcoming seminar at the Rational Intelligence Seminar Series (RISS), on July 30, 2025 (TODAY)! RISS will bring you cutting-edge talks exploring the crucial connections between AI, decision-making, and causality. Our mission with this series is to deepen our understanding of what makes machine learning systems rational, efficient, and reliable.
Join us to engage in lively discussions in the session, “Posterior Mean Matching: Generative Modeling through Online Bayesian Inference,” delivered by Yixin Wang, Assistant Professor at the University of Michigan.
Abstract
This talk will introduce posterior mean matching (PMM), a new method for generative modeling that is grounded in Bayesian inference. PMM uses conjugate pairs of distributions to model complex data of various modalities like images and text, offering a flexible alternative to existing methods like diffusion models. PMM models iteratively refine noisy approximations of the target distribution using updates from online Bayesian inference. PMM is flexible because its mechanics are based on general Bayesian models. We demonstrate this flexibility by developing specialized examples: a generative PMM model of real-valued data using the Normal-Normal model, a generative PMM model of count data using a Gamma-Poisson model, and a generative PMM model of discrete data using a Dirichlet-Categorical model. Empirically, PMMs achieve performance that is competitive with generative models for language modeling and image generation.
This is joint work with Sebastian Salazar, Michal Kucer, Emily Casleton, and David Blei.
Speaker Bio
Yixin Wang is an assistant professor of statistics at the University of Michigan. She works in the fields of Bayesian statistics, machine learning, and causal inference. Previously, she was a postdoctoral researcher with Professor Michael Jordan at the University of California, Berkeley. She completed her PhD in statistics at Columbia, advised by Professor David Blei, and her undergraduate studies in mathematics and computer science at the Hong Kong University of Science and Technology. Her research has been recognized by the NSF CAREER award, the j-ISBA Blackwell-Rosenbluth Award, ICSA Conference Young Researcher Award, ISBA Savage Award Honorable Mention, ACIC Tom Ten Have Award Honorable Mention, and INFORMS data mining and COPA best paper awards.
Logistics
Best regards,
The Rational Intelligence Lab Team
CISPA Helmholtz Center for Information Security
Speaker Bio
Yixin Wang is an assistant professor of statistics at the University of Michigan. She works in the fields of Bayesian statistics, machine learning, and causal inference. Previously, she was a postdoctoral researcher with Professor Michael Jordan at the University of California, Berkeley. She completed her PhD in statistics at Columbia, advised by Professor David Blei, and her undergraduate studies in mathematics and computer science at the Hong Kong University of Science and Technology. Her research has been recognized by the NSF CAREER award, the j-ISBA Blackwell-Rosenbluth Award, ICSA Conference Young Researcher Award, ISBA Savage Award Honorable Mention, ACIC Tom Ten Have Award Honorable Mention, and INFORMS data mining and COPA best paper awards.
Logistics
- Date: July 30, 2025 (TODAY)
- Time: 14:30 CET
- Zoom: https://cispa-de.zoom-x.de/j/61708401597
Meeting ID: 617 0840 1597
Best regards,
The Rational Intelligence Lab Team
CISPA Helmholtz Center for Information Security
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