A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models | ScienceToStartup | ScienceToStartup

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References (70)

[1]

Optimal scaling laws in learning hierarchical multi-index models

2026Leonardo Defilippis, Florent Krzakala et al.

[2]

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime

2025Leonardo Defilippis, Yizhou Xu et al.

[3]

Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws

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"This research identifies a Noise Sensitivity Exponent that links noise robustness and computational hardness in high-dimensional learning models."

Statistical Learning Theory•Score: 3•View PDF ↗

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A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models

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References (70)

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"This research identifies a Noise Sensitivity Exponent that links noise robustness and computational hardness in high-dimensional learning models."

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A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models

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References (70)

Founder's Pitch

"This research identifies a Noise Sensitivity Exponent that links noise robustness and computational hardness in high-dimensional learning models."

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