Overview
The Rational Intelligence Seminar Series (RISS) is set to host an engaging seminar featuring Yixin Wang from the University of Michigan. Scheduled for July 30, 2025, this event will delve into the intersections of artificial intelligence, decision-making, and causality, aiming to enhance our understanding of rational machine learning systems.
Background & Relevance
The field of machine learning is rapidly evolving, with Bayesian methods gaining prominence for their robustness in handling uncertainty and complexity in data. Understanding generative models through Bayesian inference is crucial as it allows researchers to create more reliable and efficient AI systems. This seminar will provide insights into how these advanced methodologies can be applied to various data modalities, including images and text, which are essential in today’s AI landscape.
Key Details
- Date: July 30, 2025
- Time: 14:30 CET
- Location: Online via Zoom
- Zoom Link: Join here
- Meeting ID: 617 0840 1597
Eligibility & Participation
This seminar is open to anyone interested in the latest advancements in machine learning and Bayesian statistics. It targets researchers, practitioners, and students who wish to deepen their understanding of generative modeling techniques.
Submission or Application Guidelines
Participation is straightforward: simply join the seminar via the provided Zoom link at the scheduled time. No prior registration is required.
More Information
The seminar will feature a presentation on posterior mean matching (PMM), a novel approach to generative modeling that leverages online Bayesian inference. PMM is designed to refine approximations of target distributions iteratively, showcasing its flexibility through various specialized examples. This approach not only competes with existing generative models but also opens new avenues for research in Bayesian statistics and machine learning.
Conclusion
This seminar represents a valuable opportunity for those in the AI and machine learning communities to engage with cutting-edge research. Attendees are encouraged to participate actively and explore the implications of PMM in their work. Don’t miss the chance to learn from Yixin Wang and contribute to the discussions surrounding these innovative methodologies.
Category: Conferences & Workshops
Tags: bayesian statistics, generative modeling, machine learning, causal inference, AI, posterior mean matching, University of Michigan, Rational Intelligence Seminar Series, data science, statistics, image generation, language modeling, online inference
