Author

Arpad Rimmel

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#machine learning Preprint Oct 2026

Mitigating Convergence Collapse in Fixed-Target Anomaly Detectors via Kernel-Anchored Locality Regularization

A family of tabular anomaly detectors trains a neural map toward a fixed target under squared-error loss and scores anomalies by the test-time residual; contraction matching, one-step rectified flow, and reconstruction autoencoders all fit this template. We characterize a convergence collapse: better optimization makes...

José Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel et al. · 0 citations

Jul 2026

Knowledge-Informed Local Causal Discovery of Optimal Adjustment Sets

Local causal discovery is a scalable alternative to global structure learning. However, it can struggle to identify valid adjustment sets in data-scarce settings because of finite-sample uncertainty, incomplete local neighborhoods, and unresolved Markov equivalence. Although many application domains provide structured...

S. Ahn, A. Leite, José Lucas De Melo Costa et al. · arXiv.org · 0 citations