Robust Analysis for Resilient AI System

• URL: http://arxiv.org/abs/2509.06172v1
• Date: Sun, 07 Sep 2025 18:47:54 GMT
• Title: Robust Analysis for Resilient AI System
• Authors: Yu Wang, Ran Jin, Lulu Kang,
• Abstract summary: Operational hazards in Manufacturing Industrial Internet (MII) systems generate severe data outliers.<n>This paper proposes a novel robust regression method, DPD-Lasso, which integrates Density Power Divergence and Lasso regularization.<n> DPD-Lasso provides reliable, stable performance on both clean and outlier-contaminated data, accurately quantifying hazard impacts.
• Score: 4.360822335264371
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Operational hazards in Manufacturing Industrial Internet (MII) systems generate severe data outliers that cripple traditional statistical analysis. This paper proposes a novel robust regression method, DPD-Lasso, which integrates Density Power Divergence with Lasso regularization to analyze contaminated data from AI resilience experiments. We develop an efficient iterative algorithm to overcome previous computational bottlenecks. Applied to an MII testbed for Aerosol Jet Printing, DPD-Lasso provides reliable, stable performance on both clean and outlier-contaminated data, accurately quantifying hazard impacts. This work establishes robust regression as an essential tool for developing and validating resilient industrial AI systems.

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