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.
Applied and Computational Harmonic Analysis , volume=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.