ArticleArXiv2025
GEM-T: Generative Tabular Data via Fitting Moments.
Miao Li, Phuc Nguyen, Christopher Tam, Alexandra Morgan, Kenneth Ge, Rahul Bansal, Linzi Yu, Rima Arnaout, Ramy Arnaout
Abstract readPreprint
In one paragraphArticle in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
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3 · Its place in the literatureWho cites it
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4 · The recordCorrections and comments
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5 · Who and what moneyAuthors and funding
9 authors.
Miao LiDepartment of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215 during the completion of this work.
Phuc NguyenDepartment of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215 during the completion of this work.
Christopher TamDepartment of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215 during the completion of this work.
Alexandra MorganDepartment of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215 during the completion of this work.
Kenneth GeDepartment of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215 during the completion of this work.
Rahul BansalDepartment of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215 during the completion of this work.
Linzi YuDepartment of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215 during the completion of this work.
Rima ArnaoutDepartment of Medicine, the Bakar Computational Health Sciences Institute, and the UCSF UC Berkeley Joint Program for Computational Precision Health at the University of California San Francisco, San Francisco, CA 94143.
Ramy ArnaoutDepartment of Pathology and the Division of Clinical Informatics, Department of Medicine, BIDMC and with Harvard Medical School, Boston, MA 02215.
Funding
Toward efficient performance for deep learning on medical imagingR01HL150394 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Rima Arnaout · 2020 to 2026
$6.2MAn Integrated Multilevel Modeling Framework for Repertoire-Based DiagnosticsR01AI148747 · NIAID · BETH ISRAEL DEACONESS MEDICAL CENTER · PI ARNAOUT, RAMY · 2020 to 2024
$2.8MNHLBI NIH HHS R01 HL150394NIAID NIH HHS R01 AI148747
6 · The paper itselfAbstract
Tabular data dominates data science but poses challenges for generative models, especially when the data is limited or sensitive. We present a novel approach to generating synthetic tabular data based on the principle of maximum entropy-MaxEnt-called GEM-T, for "generative entropy maximization for tables." GEM-T directly captures
Identifiers
PMID41356061
PMCPMC12679823
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