Harmonic Machine Learning Models are Robust
<|reference_start|>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, 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.<|reference_end|>
@article{kersting2024harmonic,
    title={Harmonic Machine Learning Models are Robust},
    author={Nicholas S. Kersting, Yi Li, Aman Mohanty, Oyindamola Obisesan,
  Raphael Okochu},
    journal={arXiv preprint arXiv:2404.18825},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18825},
    primaryClass={cs.LG cs.AI cs.CV}
}