Overview
The Rational Intelligence Seminar Series (RISS) is set to host an engaging session featuring Yixin Wang from the University of Michigan. This seminar will delve into the innovative intersections of artificial intelligence, decision-making, and causality, aiming to enhance our comprehension of rationality in machine learning systems.
Background & Relevance
Generative modeling is a pivotal area within machine learning, focusing on creating models that can generate new data instances that resemble a given dataset. This field is crucial for applications ranging from image synthesis to natural language processing. The integration of Bayesian inference into generative modeling offers a robust framework for handling uncertainty and improving model reliability. Understanding these concepts is vital for advancing AI technologies that are efficient and trustworthy.
Key Details
- Date: July 30, 2025
- Time: 14:30 CET
- Location: Online (Zoom link provided)
- Session Title: Posterior Mean Matching: Generative Modeling through Online Bayesian Inference
- Speaker: Yixin Wang, Assistant Professor, University of Michigan
- Link to Seminar: RISS Seminar Series
- Zoom Meeting ID: 617 0840 1597
- Zoom Link: Join Here
Eligibility & Participation
This seminar is open to all individuals interested in the fields of machine learning, statistics, and AI. It is particularly relevant for researchers, practitioners, and students who wish to deepen their understanding of generative modeling and Bayesian methods.
Submission or Application Guidelines
No prior registration is required to attend the seminar. Participants can join directly through the provided Zoom link at the scheduled time. It is encouraged to prepare questions and engage in discussions during the session to maximize the learning experience.
More Information
The Rational Intelligence Seminar Series aims to foster a deeper understanding of rationality in AI systems through discussions and presentations from leading experts in the field. By exploring topics such as Bayesian inference and generative modeling, the series contributes to the ongoing dialogue about the future of intelligent systems and their applications across various domains.
Conclusion
This seminar presents a valuable opportunity for anyone interested in the latest advancements in generative modeling and Bayesian statistics. Attendees are encouraged to participate actively and share insights from the discussion. Join us for an enlightening session that promises to enhance your understanding of these critical areas in AI.
Category: Conferences & Workshops
Tags: bayesian statistics, generative modeling, machine learning, causal inference, posterior mean matching, AI, decision-making, Rational Intelligence Seminar, University of Michigan, CISPA, online inference, data science
