2 citations · 4 across the 6 of their papers we have counts for
7 papers
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…
Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
Leonardo Defilippis, Yizhou Xu, Julius Girardin +5
Neural scaling laws underlie many of the recent advances in deep learning, yet their theoretical understanding remains largely confined to linear models. In this work, we present a…
Optimal Spectral Transitions in High-Dimensional Multi-Index Models
Leonardo Defilippis, Yatin Dandi, Pierre Mergny +2
We consider the problem of how many samples from a Gaussian multi-index model are required to weakly reconstruct the relevant index subspace. Despite its increasing popularity as a…
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…
Fundamental computational limits of weak learnability in high-dimensional multi-index models
Emanuele Troiani, Yatin Dandi, Leonardo Defilippis +3
Multi-index models - functions which only depend on the covariates through a non-linear transformation of their projection on a subspace - are a useful benchmark for investigating…
High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
Urte Adomaityte, Leonardo Defilippis, Bruno Loureiro +1
We investigate the high-dimensional properties of robust regression estimators in the presence of heavy-tailed contamination of both the covariates and response functions. In parti…