Harmonic Machine Learning Models are Robust
machine learning; robustness; explainable AI; adversarial attacks
The paper is a dual submission with AISTATS paper #1812. Dual submissions are not permitted at ICLR.
Submission Desk Rejected by Program Chairs
Conformity to the mathematical property of being harmonic used as a metric to measure model robustness
@misc{
kersting2024harmonic,
title={Harmonic Machine Learning Models are Robust},
author={Nicholas Stephen Kersting and Yi Li and Aman Mohanty and Oyindamola Obisesan and Raphael Okochu},
year={2024},
url={https://openreview.net/forum?id=UcFjiiObbM}
}
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.
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~Nicholas_Stephen_Kersting2 ~Yi_Li42 ~Aman_Mohanty1 ~Oyindamola_Obisesan1 ~Raphael_Okochu1
Nicholas Stephen Kersting Yi Li Aman Mohanty Oyindamola Obisesan Raphael Okochu
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machine learning; robustness; explainable AI; adversarial attacks
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kersting|harmonic_machine_learning_models_are_robust
/pdf/d1412b8046cfb94a2723adaa3f54d91be09e576c.pdf
other topics in machine learning (i.e., none of the above)
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Harmonic Machine Learning Models are Robust
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission