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Coffee and Check-In
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Opening Remarks
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Tutorial: Statistical Learning Theory, Optimization, and Neural Networks I
Spencer Frei (UC Davis)
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Break
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Tutorial: Statistical Learning Theory, Optimization, and Neural Networks II
Spencer Frei (UC Davis)
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Lunch
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Understanding the Robustness of Deep Learning
Aditi Raghunathan (Stanford)
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Break
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Feature Selection with Gradient Descent on Two-layer Networks in Low-rotation Regimes
Matus Telgarsky (New York University)
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Reception
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Coffee and Check-In
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Tutorial: Implicit Bias I
Nati Srebro (Toyota Technological Institute at Chicago)
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Break
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Tutorial: Implicit Bias II
Nati Srebro (Toyota Technological Institute at Chicago)
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Lunch
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The Devil is in the Tails and Other Stories of Interpolation
Niladri Chatterji (UC Berkeley)
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Break
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Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting
Neil Mallinar (UCSD), Preetum Nakkiran (Apple & UC San Diego)
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Coffee and Check-In
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Tutorial: Methods from Statistical Physics I
Ahmed El Alaoui (Simons Institute, UC Berkeley )
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Break
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Tutorial: Methods from Statistical Physics II
Ahmed El Alaoui (Simons Institute, UC Berkeley )
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Lunch
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Panel: Perspectives on Machine Learning and Statistics
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Break
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Distribution Shift as Underspecification, and What We Might Do About It
Chelsea Finn (Stanford)
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Break
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A New Perspective on High-Dimensional Causal Inference
Pragya Sur (Harvard)
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Coffee and Check-In
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Tutorial: Methods from Statistical Physics III
Ahmed El Alaoui (Simons Institute, UC Berkeley )
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Break
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Tutorial: Training & Deploying Multi-Modal Models
Brandon McKinzie (Apple)
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Lunch
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A Theoretical Framework of Convolutional Kernels on Image Datasets
Song Mei (UC Berkeley)
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Break
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Universality of Approximate Message Passing on Semi-random Matrices
Subhabrata Sen (Harvard University)
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Coffee and Check-In
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When is Scale Enough?
Ethan Dyer (Google Research, Blueshift Team)
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Break
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Deep Learning in Structural Biology and Protein Design: How, Where, and Why 
Chloe Hsu (UC Berkeley)
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Concluding Remarks