Showing stat.MLShow all
3 papers · 1 filter
stat.ML2026
Asymptotic Theory of Iterated Empirical Risk Minimization, with Applications to Active Learning
Hugo Cui, Yue M. Lu
We study a class of iterated empirical risk minimization (ERM) procedures in which two successive ERMs are performed on the same dataset, and the predictions of the first estimator…
stat.ML2025
Pretrain-Test Task Alignment Governs Generalization in In-Context Learning
Mary I. Letey, Jacob A. Zavatone-Veth, Yue M. Lu +1
In-context learning (ICL) is a central capability of Transformer models, but the structures in data that enable its emergence and govern its robustness remain poorly understood. In…
stat.ML2024
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
Yatin Dandi, Luca Pesce, Hugo Cui +3
A key property of neural networks is their capacity of adapting to data during training. Yet, our current mathematical understanding of feature learning and its relationship to gen…