Evidence map›Paper›PMID 35754924›Full record

ArticleJournal of machine learning research : JMLR

Soft Tensor Regression.

Georgia Papadogeorgou, Zhengwu Zhang, David B Dunson

Open access · greenAbstract read
PubMed Full text at PMC
In one paragraph

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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 registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 0 citations in OpenAlex.

  1. Efficient Decision Trees for Tensor Regressions.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Georgia PapadogeorgouDepartment of Statistics, University of Florida, Gainesville, FL 32611-8545, USA.
Zhengwu ZhangDepartment of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3260, USA.
David B DunsonDepartment of Statistical Science, Duke University, Durham, NC 27708-0251, USA.

Funding

Reproducibility and Robustness of Dimensionality ReductionR01ES027498 · NIEHS · DUKE UNIVERSITY · PI HERRING, AMY H · 2017 to 2021
$2.9M
Structured nonparametric methods for mixtures of exposuresR01ES028804 · NIEHS · DUKE UNIVERSITY · PI DUNSON, DAVID BRIAN · 2018 to 2021
$2.2M
CRCNS: Geometry-based Brain Connectome AnalysisR01MH118927 · NIMH · DUKE UNIVERSITY · PI DUNSON, DAVID BRIAN, ZHANG, ZHENGWU · 2018 to 2020
$945k
NIEHS NIH HHS R01 ES027498NIEHS NIH HHS R01 ES028804NIMH NIH HHS R01 MH118927
6 · The paper itself

Abstract

Statistical methods relating tensor predictors to scalar outcomes in a regression model generally vectorize the tensor predictor and estimate the coefficients of its entries employing some form of regularization, use summaries of the tensor covariate, or use a low dimensional approximation of the coefficient tensor. However, low rank approximations of the coefficient tensor can suffer if the true rank is not small. We propose a tensor regression framework which assumes a

Indexed as

adjacency matrixBayesianbrain connectomicsgraph datalatent factorslow ranknetwork dataparafactensor regression

Identifiers

PMID35754924
PMCPMC9222480
OpenAlexW2981716445

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.