S2MAM is a new semi-supervised model that uses bilevel optimization to automatically identify informative variables, update similarity matrices, and provide interpretable predictions with theoretical guarantees.
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S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
S2MAM is a new semi-supervised model that uses bilevel optimization to automatically identify informative variables, update similarity matrices, and provide interpretable predictions with theoretical guarantees.