S2MAM uses a probabilistic bilevel optimization scheme to learn binary masks on input variables, simultaneously performing variable selection and adaptive graph Laplacian construction for robust semi-supervised additive regression.
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On five tabular security datasets at 10% labels, tuning only the classifier with Bayesian optimization recovers a median 86% of the gains from full joint SSL-classifier optimization.
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S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
S2MAM uses a probabilistic bilevel optimization scheme to learn binary masks on input variables, simultaneously performing variable selection and adaptive graph Laplacian construction for robust semi-supervised additive regression.
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SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification
On five tabular security datasets at 10% labels, tuning only the classifier with Bayesian optimization recovers a median 86% of the gains from full joint SSL-classifier optimization.