3 papers
cs.LG2025
How does training shape the Riemannian geometry of neural network representations?
Jacob A. Zavatone-Veth, Sheng Yang, Julian A. Rubinfien +1
In machine learning, there is a long history of trying to build neural networks that can learn from fewer example data by baking in strong geometric priors. However, it is not alwa…
stat.ML2025
Asymptotic theory of in-context learning by linear attention
Yue M. Lu, Mary I. Letey, Jacob A. Zavatone-Veth +2
Transformers have a remarkable ability to learn and execute tasks based on examples provided within the input itself, without explicit prior training. It has been argued that this…
stat.ML2025
Scaling and renormalization in high-dimensional regression
Alexander Atanasov, Jacob A. Zavatone-Veth, Cengiz Pehlevan
From benign overfitting in overparameterized models to rich power-law scalings in performance, simple ridge regression displays surprising behaviors sometimes thought to be limited…