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stat.ML2025
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…
stat.ML2025
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…