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Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives

Journal of the Royal Statistical Society Series B (Statistical Methodology)(2025)10.1093/jrsssb/qkaf076Source: DataRank Database

Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives is a research paper published in Journal of the Royal Statistical Society Series B (Statistical Methodology) (2025). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (12 grants)Impact (FWCI 0.00)OA: greenTopics (26)IDs (PubMed, OpenAlex)

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Accessible (1/2)

Open Access

Interoperable (0/2)

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Authors (5)

Gabriel Loewinger ORCID,Kenneth T. Kishida ORCID,Giovanni Parmigiani ORCID,Rahul Mazumder,Kayhan Behdin ORCID

Live enrichment

PubMed:42459560 OpenAlex:https://openalex.org/W4417450047

NIDA

Grant: F31DA052153

NIDA

Grant: R01DA048096

NSF

Grant: 1810829

Statistical Methods for Multi-Study Predictions

NSF

Grant: 2113707

Advancing Statistical Methods for Multi-Study Predictions

NSF

Grant: IIS-1718258

ONR

Grant: N000142112841

NIMH

Grant: R01MH121099, R01MH124115

NIH

Grant: 5KL2TR001420

National Institutes of Health

Grant: 1F31DA052153-01

Multi-Study Integer Programming Methods for Human Voltammery

National Institutes of Health

Grant: 5R01DA048096-02

Real-time neurochemical encoding of reward- and punishment-prediction errors and associated subjective experiences in humans

National Institutes of Health

Grant: 5R01MH121099-04

Neuro-computational Approach to Determine a Neurochemical Basis of Mood and Depression

National Institutes of Health

Grant: 5R01MH124115-05

Computational and electrochemical substrates of social decision-making in humans

FWCI

0.00

Citation Percentile

0.1%

greenLicense: public-domain

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC13370345/

publisherhttps://academic.oup.com/jrsssb/article-pdf/88/3/1029/65926219/qkaf076.pdf

journalhttps://doi.org/10.1093/jrsssb/qkaf076

repositoryhttps://pubmed.ncbi.nlm.nih.gov/42459560

Fields of Study

Domain Adaptation and Few-Shot LearningMachine Learning in HealthcareSparse and Compressive Sensing Techniques0202 electrical engineering, electronic engineering, information engineering02 engineering and technology0101 mathematics01 natural sciences

Keywords

Leverage (statistics)Feature selectionCoordinate descentEstimatorNoveltySelection (genetic algorithm)ScalabilityLinear regressionVariable (mathematics)Variable selectionMulti-task LearningMixed-integer ProgrammingSparse Linear RegressionError BoundsMachine LearningMethodology (stat.ME)FOS: Computer and information sciencesMethodologyMachine Learning (stat.ML)