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ARXIV:2604.19072 · STATISTICAL MODELING · SUBMITTED 22 APR · 02:16 UTC · FRESHNESS STALE
ARXIV:2604.19072STATISTICAL MODELINGSUBMITTED 22 APR · 02:16 UTCFRESHNESS STALEXuelin Zhang · Hong Chen · Yingjie Wang · Tieliang Gong · Bin Gu · arXiv
A semi-supervised meta-additive model that automatically identifies informative variables and updates similarity matrices for robust predictions.
Opportunity summary
Pain A semi-supervised meta-additive model that automatically identifies informative variables and updates similarity matrices for robust predictions.
Evidence 63 refs | 3 sources | 67% coverage
Blocker Evidence unverified
A semi-supervised meta-additive model that automatically identifies informative variables and updates similarity matrices for robust predictions. Typically, the Laplace-Beltrami operator-based manifold regularization can be approximated empirically by the Laplacian regularization associated with the entire…
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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…
Statistical Modeling moved forward this cycle; last verified April 2026. Public score 3.0/10. Production flags indicate code availability.
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A semi-supervised meta-additive model that automatically identifies informative variables and updates similarity matrices for robust predictions.
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Paper Pack
10.48550/arXiv.2604.19072A semi-supervised meta-additive model that automatically identifies informative variables and updates similarity matrices for robust predictions.
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 approximated empirically by the Laplacian regularization associated with the entire training data and its corresponding graph Laplacian matrix. However, the graph Laplacian matrix depends heavily on the prespecified similarity metric and may lead to inappropriate penalties when dealing with redundant or noisy input variables. To address the above issues, this paper proposes a new \textit{Semi-Supervised Meta Additive Model (S$^2$MAM) based on a bilevel optimization scheme that automatically identifies informative variables, updates the similarity matrix, and simultaneously achieves interpretable predictions. Theoretical guarantees are provided for S$^2$MAM, including the computing convergence and the statistical generalization bound. Experimental assessments across 4 synthetic and 12 real-world datasets, with varying levels and categories of corruption, validate the robustness and interpretability of the proposed approach.
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Proof status
unverified63 refs; 3 sources; 67% coverage.
What was readable
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PROBLEM
A semi-supervised meta-additive model that automatically identifies informative variables and updates similarity matrices for robust predictions. Typically, the Laplace-Beltrami operator-based manifold regularization can be approximated empirically by the Laplacian regularizatio...
METHOD
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. Typi...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. 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 margi...
WHY NOW
Statistical Modeling moved forward this cycle; last verified April 2026. Public score 3.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 11, "author": "Xuelin Zhang; Hong Chen; Yingjie Wang; Tieliang Gong; Bin Gu"
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A semi-supervised meta-additive model that automatically identifies informative variables and updates similarity matrices for robust predictions.
Segment
Statistical Modeling
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Commercial read
3.0/10 public viability
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Evidence coverage
OpportunityKernel evidence_receipt
63 refs / 3 sources / 67% coverage
stale
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Evidence
63 references, 3 sources, 67% evidence coverage.
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