The Cost of Robustness: Tighter Bounds on Parameter Complexity for Robust Memorization in ReLU Nets

📝 Original Info

• Title: The Cost of Robustness: Tighter Bounds on Parameter Complexity for Robust Memorization in ReLU Nets
• ArXiv ID: 2510.24643
• Date: 2025-10-28
• Authors: 정보 없음 (논문에 저자 정보가 제공되지 않음)

📝 Abstract

We study the parameter complexity of robust memorization for $\mathrm{ReLU}$ networks: the number of parameters required to interpolate any given dataset with $ε$-separation between differently labeled points, while ensuring predictions remain consistent within a $μ$-ball around each training sample. We establish upper and lower bounds on the parameter count as a function of the robustness ratio $ρ= μ/ ε$. Unlike prior work, we provide a fine-grained analysis across the entire range $ρ\in (0,1)$ and obtain tighter upper and lower bounds that improve upon existing results. Our findings reveal that the parameter complexity of robust memorization matches that of non-robust memorization when $ρ$ is small, but grows with increasing $ρ$.

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