4 papers
Fast Escape, Slow Convergence: Learning Dynamics of Phase Retrieval under Power-Law Data
Guillaume Braun, Bruno Loureiro, Ha Quang Minh +1
Scaling laws describe how learning performance improves with data, compute, or training time, and have become a central theme in modern deep learning. We study this phenomenon in a…
Geometric Jensen-Shannon Divergence Between Gaussian Measures On Hilbert Space
Minh Ha Quang, Frank Nielsen
This work studies the Geometric Jensen-Shannon divergence, based on the notion of geometric mean of probability measures, in the setting of Gaussian measures on an infinite-dimensi…
Categorical and geometric methods in statistical, manifold, and machine learning
Hông Vân Lê, Hà Quang Minh, Frederic Protin +1
We present and discuss applications of the category of probabilistic morphisms, initially developed in \cite{Le2023}, as well as some geometric methods to several classes of proble…
Learning a Single Index Model from Anisotropic Data with vanilla Stochastic Gradient Descent
Guillaume Braun, Minh Ha Quang, Masaaki Imaizumi
We investigate the problem of learning a Single Index Model (SIM)- a popular model for studying the ability of neural networks to learn features - from anisotropic Gaussian inputs…