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New S2MAM model enhances semi-supervised learning with robust variable selection

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 3 sources

Researchers have introduced the Semi-Supervised Meta Additive Model (S2MAM), a novel approach designed to enhance semi-supervised learning. S2MAM utilizes a bilevel optimization scheme to automatically identify important variables and refine the similarity matrix, leading to more interpretable predictions. The method has demonstrated robustness and interpretability across numerous synthetic and real-world datasets, with theoretical guarantees provided for its convergence and generalization. AI

Summary written by gemini-2.5-flash-lite from 3 sources. How we write summaries →

RANK_REASON This is a research paper detailing a new model and its experimental validation.

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New S2MAM model enhances semi-supervised learning with robust variable selection

COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 ·

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

    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. Typic…

  2. arXiv stat.ML TIER_1 · Xuelin Zhang, Hong Chen, Yingjie Wang, Tieliang Gong, Bin Gu ·

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

    arXiv:2604.19072v2 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 h…

  3. arXiv stat.ML TIER_1 · Bin Gu ·

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

    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. Typic…

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