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This paper investigates the prediction risk of bagging methods in the context of overparameterized learning. It provides a theoretical framework for understanding the behavior of bagged predictors under the proportional asymptotics regime, discussing variants such as subagging and splagging. The authors derive exact asymptotic risks of bagged ridge and ridgeless predictors and propose a cross-validation strategy for optimal subsampling, demonstrating how these techniques can mitigate the risk...
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The Journal of Machine Learning is a scholarly publication that focuses on the field of machine learning, covering a wide range of topics including algorithms, theory, applications, and interdisciplinary approaches.
Researchers, academics, and practitioners in the field of machine learning are typically required to submit their findings and studies to the Journal of Machine Learning for publication consideration.
To fill out a submission for the Journal of Machine Learning, authors must prepare their manuscripts according to the journal's submission guidelines, including properly formatted text, figures, and bibliographic references.
The purpose of the Journal of Machine Learning is to disseminate high-quality research findings in the field, foster scientific discourse, and advance the understanding of machine learning methodologies and applications.
Authors must report comprehensive details including the research problem, methodology, results, discussions, and conclusions, as well as referencing prior works and relevant literature.
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