All talks listed in Pacific Time. Schedule subject to change.

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Coffee and Check-In
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Opening Remarks
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Detection and Steering in LLMs using Feature Learning

Daniel Beaglehole (UC San Diego)
Video
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Break
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Adam and friends via duality

Matus Telgarsky (NYU)
Video
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Lunch (on your own)
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Theory of Scaling: A General Framework for Scale-aware Training

Soufiane Hayou (UC Berkeley)
Video
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Break
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A Theory of Learning with Autoregressive Chain of Thought

Nirmit Joshi (TTIC)
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Reception and Poster Session (2nd Floor)
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Coffee and Check-In
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A Statistical Theory of Contrastive Pre-training and Multimodal Generative AI

Song Mei (UC Berkeley)
Video
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Break
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Generalization and overfitting in two-layer neural networks

Pierfrancesco Urbani (CNRS)
Video
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Lunch (on your own)
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Emergence and scaling laws for SGD learning and Learning Compositional Functions with Transformers

Jason Lee (Princeton University)
Video
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Break
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Local minima of the empirical risk in high dimension

Basil Saeed (Stanford University)
Video
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Weak-to-Strong Generalization Even in Random Feature Networks, Provably

Marko Medvedev (University of Chicago)
Video
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Happy Hour at Freehouse
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Coffee and Check-In
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Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression

Jingfeng Wu (UC Berkeley)
Video
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Break
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Neural networks for PDE solvers: representational power and inductive biases

Andrej Riteski (Carnegie Mellon University)
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Lunch (on your own)
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Pencil: Long Thoughts with Short Memory

Zhiyuan Li (TTIC)
Video
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Break
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Positive Distribution Shift

Nati Srebro (TTIC)
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Panel Discussion

Bin Yu (UC Berkeley),
Andrej Riteski (Carnegie Mellon University),
Matus Telgarsky (NYU)