Stanford University
Random Matrices as a Model of Discrete Quantum Chaos |
Harvard University
Learning Hard Problems with Neural Networks and Language Models |
Stanford University
Machine learning for aging and spatial omics |
University of Chicago
Topics in Sequential Anytime-Valid Inference: Comparing Forecasters & Combining Evidence |
University of Michigan
Statistical and Computational Methods for Genetic and Genomic Studies |
Princeton
Principled Algorithms for Efficient Machine Learning |
Stanford University
Phase Transitions and Algorithmic Aspects of the Binary Perceptron |
MIT
Learning Theoretic Foundations for Modern (Data) Science |
Stanford University
Supporting Policies and Interventions to Promote Healthy and Sustainable Habits |
Massachusetts Institute of Technology
Asymptotics of high-dimensional Bayesian inference |
MIT
Algorithmic Thresholds in Random Optimization Problems |
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Miller Institute, Berkeley
Efficiently learning and sampling from multimodal distributions using data-based initialization |
Harvard University
Scaling Limits and Scaling Laws of Deep Learning |
Yale University
Unveiling In-Context Learning: Provable Training Dynamics and Feature Learning in Transformers |
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Princeton University
Sharp matrix concentration inequalities |
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Wharton University of Pennsylvania
Do Large Language Models Need Statistical Foundations? |
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University of Cambridge
TBA |
