S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection 事件

S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection arXiv:2604.19072v3 Announce Type: replace-cross Abstract: Semi-supervised learning with manifold regularization is a classical framework for jointly learning from both labeled and unlabeled data, where the key requirement is that the support of the unknown marginal distribution has the geometric structure of a Riemannian manifold. Typically, the Laplace-Beltrami operator-based manifold regularization can be