Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications
In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our understanding of fundamental phenomena. Increasingly, deep learning models are applied to analyze both simulated and experimental data. In most experiments, a rigorous regime of testing for physically motivated systematic uncertainties is in place. The numerical evaluation of these tests for differences between the data on the one side and simulations on the other side quantifies the effect of potential sources of mismodelling on the machine learning output. In addition, thorough comparisons of marginal distributions and (linear) feature correlations...
| Authors | Philip Bechtle, Lucie Flek, Philipp Alexander Jung et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 March 2026 |
| Last updated at source | 8 April 2026 |
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