2 citations · 4 across the 6 of their papers we have counts for
3 papers · 1 filter
A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models
Leonardo Defilippis, Florent Krzakala, Bruno Loureiro +1
Understanding when learning is statistically possible yet computationally hard is a central challenge in high-dimensional statistics. In this work, we investigate this question in…
Optimal scaling laws in learning hierarchical multi-index models
Leonardo Defilippis, Florent Krzakala, Bruno Loureiro +1
In this work, we provide a sharp theory of scaling laws for two-layer neural networks trained on a class of hierarchical multi-index targets, in a genuinely representation-limited…
Dimension-free deterministic equivalents and scaling laws for random feature regression
Leonardo Defilippis, Bruno Loureiro, Theodor Misiakiewicz
In this work we investigate the generalization performance of random feature ridge regression (RFRR). Our main contribution is a general deterministic equivalent for the test error…