Harmonic Machine Learning Models are Robust

We introduce Harmonic Robustness, a powerful and intuitive method to test the robustness of any machine-learning model either during training or in black-box real-time inference monitoring without ground-truth labels. It is based on functional deviation from the harmonic mean value property, indicat...

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Bibliographic Details
Published inarXiv.org
Main Authors Kersting, Nicholas S, Li, Yi, Mohanty, Aman, Obisesan, Oyindamola, Okochu, Raphael
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.04.2024
Subjects
Machine learning
Robustness
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Summary:We introduce Harmonic Robustness, a powerful and intuitive method to test the robustness of any machine-learning model either during training or in black-box real-time inference monitoring without ground-truth labels. It is based on functional deviation from the harmonic mean value property, indicating instability and lack of explainability. We show implementation examples in low-dimensional trees and feedforward NNs, where the method reliably identifies overfitting, as well as in more complex high-dimensional models such as ResNet-50 and Vision Transformer where it efficiently measures adversarial vulnerability across image classes.
ISSN:2331-8422