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stat.ML2026
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…
stat.ML2026
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…