HRT reframes oblique splits as Newton-optimized hinge regressions achieving universal approximation with O(δ²) rate, and HRT-Boost ensembles them with stage-wise empirical risk reduction guarantees under squared loss.
Tabnet: Attentive interpretable tabular learn- ing
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Static malware classifiers learn packing artifacts and dataset composition biases rather than malicious semantics, as diagnosed by TRUSTEE interpretability across controlled dataset variations.
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Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models
HRT reframes oblique splits as Newton-optimized hinge regressions achieving universal approximation with O(δ²) rate, and HRT-Boost ensembles them with stage-wise empirical risk reduction guarantees under squared loss.
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Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE
Static malware classifiers learn packing artifacts and dataset composition biases rather than malicious semantics, as diagnosed by TRUSTEE interpretability across controlled dataset variations.