Kolmogorov--Arnold Networks as Implicit Regularizers: Noise Robustness and Interpretability for Stellar Classification
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| Format: | Preprint |
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2026
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| _version_ | 1866913168783048704 |
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| author | Sestak, Kristian |
| author_facet | Sestak, Kristian |
| contents | This paper tests whether Kolmogorov--Arnold Networks (KAN 2.0) are genuinely more noise-robust than Multi-Layer Perceptrons (MLP) and XGBoost for stellar classification (star/galaxy/quasar, 100,000 SDSS DR17 objects). A naive comparison suggests so: KAN retains +9 percentage points over MLP at SNR=5. But equalizing baseline accuracy via weight decay eliminates the gap -- a properly regularized MLP matches KAN to within 1 p.p. at all SNR levels, both with and without spectroscopic redshift. The same holds on an independent DESI DR1 sample with different photometric bands. KAN's robustness thus traces to implicit regularization by C^2-smooth B-spline activations, not to architecture. Per-class analysis (20 trials) shows that stars degrade fastest (F1: 0.97 to 0.75 at SNR=5), while QSOs remain stable. KAN's native feature importance and SHAP on MLP produce different rankings (Spearman rho = -0.37), capturing complementary aspects of the classification. Colour-index features (u-g, g-r, r-i, i-z) widen KAN's relative advantage, and a hybrid pipeline routing uncertain MLP predictions to KAN improves low-SNR accuracy. KAN is best understood as a convenient auto-regularizer whose genuine advantage is built-in interpretability. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_29039 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Kolmogorov--Arnold Networks as Implicit Regularizers: Noise Robustness and Interpretability for Stellar Classification Sestak, Kristian Instrumentation and Methods for Astrophysics This paper tests whether Kolmogorov--Arnold Networks (KAN 2.0) are genuinely more noise-robust than Multi-Layer Perceptrons (MLP) and XGBoost for stellar classification (star/galaxy/quasar, 100,000 SDSS DR17 objects). A naive comparison suggests so: KAN retains +9 percentage points over MLP at SNR=5. But equalizing baseline accuracy via weight decay eliminates the gap -- a properly regularized MLP matches KAN to within 1 p.p. at all SNR levels, both with and without spectroscopic redshift. The same holds on an independent DESI DR1 sample with different photometric bands. KAN's robustness thus traces to implicit regularization by C^2-smooth B-spline activations, not to architecture. Per-class analysis (20 trials) shows that stars degrade fastest (F1: 0.97 to 0.75 at SNR=5), while QSOs remain stable. KAN's native feature importance and SHAP on MLP produce different rankings (Spearman rho = -0.37), capturing complementary aspects of the classification. Colour-index features (u-g, g-r, r-i, i-z) widen KAN's relative advantage, and a hybrid pipeline routing uncertain MLP predictions to KAN improves low-SNR accuracy. KAN is best understood as a convenient auto-regularizer whose genuine advantage is built-in interpretability. |
| title | Kolmogorov--Arnold Networks as Implicit Regularizers: Noise Robustness and Interpretability for Stellar Classification |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2605.29039 |