ArticleJournal of machine learning research : JMLR
Generalized Sparse Additive Models.
Article in Journal of machine learning research : JMLR. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Doubly regularized generalized linear models for spatial observations with high-dimensional covariates.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Decoding longitudinal microbiome trajectories: an interpretable machine learning approach for biomarker discovery and prediction.Briefings in bioinformatics · 2025Article
- Longitudinal Microbiome-based Interpretable Machine Learning for Identification of Time-Varying Biomarkers in Early Prediction of Disease Outcomes.bioRxiv : the preprint server for biology · 2024Article
- Exploring and Interacting with the Set of Good Sparse Generalized Additive Models.Advances in neural information processing systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We present a unified framework for estimation and analysis of generalized additive models in high dimensions. The framework defines a large class of penalized regression estimators, encompassing many existing methods. An efficient computational algorithm for this class is presented that easily scales to thousands of observations and features. We prove minimax optimal convergence bounds for this class under a weak compatibility condition. In addition, we characterize the rate of convergence when this compatibility condition is not met. Finally, we also show that the optimal penalty parameters for structure and sparsity penalties in our framework are linked, allowing cross-validation to be conducted over only a single tuning parameter. We complement our theoretical results with empirical studies comparing some existing methods within this framework.
Indexed as
Identifiers
37873545PMC10593424What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.