We are the National AI Institute for Foundations of Machine Learning (IFML)
Designated by the National Science Foundation (NSF) in 2020, IFML develops the key foundational tools for the next decade of AI innovation. Our institute comprises researchers from The University of Texas at Austin, University of Washington, Wichita State University, and Microsoft Research.
Our researchers create new algorithms that can help machines learn on the fly, change their expectations as they encounter people and objects in real life, and even bounce back from deliberate attempts by adversaries to manipulate datasets.
Featured
IFML Seminar
Foundation Model for Sequential Decision-Making
Abstract: Sequential decision-making (SDM) is crucial for adapting machine learning to dynamic real-world scenarios such as fluctuating markets or evolving healthcare, requiring models that can effectively navigate ongoing changes. Foundation models, akin to those...
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That's a Wrap! Texas Summer Academy for Machine Learning
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Smoothed Analysis for Learning Concepts with Low Intrinsic Dimension earns Best Paper Award at COLT 2024!
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IFML Podcast
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Ambient Diffusion: Reducing Dataset Memorization by Training with Heavily Corrupted Data
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IFML research collaborations power next generation of multimodal AI with OpenCLIP and DataComp
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NLP Modules for High School
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Upcoming Events and Workshops
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September27
Computationally Efficient Reinforcement Learning with Linear Bellman Completeness
Talk by Noah Golowich , PhD Student, MIT
October4Foundation Model for Sequential Decision-Making
Talk by Furong Huang, Associate Professor, University of Maryland
Previously Recorded Talks
New & Noteworthy
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Now, Later, and Lasting: 10 Priorities for AI Research, Policy, and Practice
Shaping the future of artificial intelligence
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THIS JUST IN: Special issue of AI Magazine celebrates National AI Institutes
IFML: AI systems that will transform our world
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Will Robots Triumph over World Cup Winners by 2050?
The RoboCup dream has inspired generations of roboticists