{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:34:52Z","timestamp":1750296892200,"version":"3.37.3"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Department of National Defences Innovation for Defence Excellence, and Security"},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["2017-260 250"],"award-info":[{"award-number":["2017-260 250"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Signal Process."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tsp.2020.3038480","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T20:39:45Z","timestamp":1605645585000},"page":"489-499","source":"Crossref","is-referenced-by-count":1,"title":["Learning Gaussian Graphical Models With Ordered Weighted $\\ell _1$ Regularization"],"prefix":"10.1109","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7109-8776","authenticated-orcid":false,"given":"Cody","family":"Mazza-Anthony","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5030-1379","authenticated-orcid":false,"given":"Bogdan","family":"Mazoure","sequence":"additional","affiliation":[]},{"given":"Mark","family":"Coates","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","first-page":"1059?1062","article-title":"The huge package for high-dimensional undirected graph estimation in R","volume":"13","author":"zhao","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"2293?2326","DOI":"10.1214\/12-AOS1037","article-title":"High-dimensional semiparametric gaussian copula graphical models","volume":"40","author":"liu","year":"2012","journal-title":"Ann Statist"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/S0046-8177(88)80496-9"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1093\/nar\/gkt1102","article-title":"The reactome pathway knowledgebase","volume":"42","author":"croft","year":"2013","journal-title":"Nucleic Acids Res"},{"article-title":"Inequalities. Cambridge Mathematical Library Series","year":"1967","author":"hardy","key":"ref36"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1017\/9781108627771"},{"year":"2019","key":"ref34","article-title":"Global Industry Classification Standard (GICS)"},{"key":"ref10","first-page":"939?947","article-title":"Inferring block structure of graphical models in exponential families","author":"sun","year":"0","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref11","first-page":"1035?1062","article-title":"Joint estimation of multiple precision matrices with common structures","volume":"16","author":"lee","year":"2015","journal-title":"J Mach Learn Res"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.5705\/ss.2013.192"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.5705\/ss.2014.256"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2016.1247002"},{"article-title":"A unified framework for structured graph learning via spectral constraints","year":"2019","author":"kumar","key":"ref15"},{"key":"ref16","article-title":"Estimation of graphical models through structured norm minimization","volume":"18","author":"tarzanagh","year":"2018","journal-title":"J Mach Learn Res"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2007.00843.x"},{"journal-title":"Probabilistic Graphical Models Principles and Techniques","year":"2009","author":"koller","key":"ref18"},{"key":"ref19","first-page":"1265?1273","article-title":"Sparse gaussian conditional random fields: Algorithms, theory, and application to energy forecasting","author":"wytock","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref28","first-page":"2825?2830","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref4","first-page":"3808?3816","article-title":"Learning sparse Gaussian graphical models with overlapping blocks","author":"hosseini","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","first-page":"2330?2338","article-title":"Sparse inverse covariance matrix estimation using quadratic approximation","author":"hsieh","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxm045"},{"key":"ref6","first-page":"1250?1258","article-title":"A convex formulation for learning scale-free networks via submodular relaxation","author":"defazio","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"531?537","DOI":"10.1126\/science.286.5439.531","article-title":"Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring","volume":"286","author":"golub","year":"1999","journal-title":"Science"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2014.11.015"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2011.tm10155"},{"key":"ref7","first-page":"153?160","article-title":"Projected subgradient methods for learning sparse gaussians","author":"duchi","year":"0","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1000479"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1214\/009053606000000281"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/ecs2.1231"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00503.x"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOAS842"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00490.x"},{"key":"ref24","first-page":"1574?1582","article-title":"Iterative thresholding algorithm for sparse inverse covariance estimation","author":"rolfs","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","first-page":"930?938","article-title":"Ordered weighted $\\ell _1$ regularized regression with strongly correlated covariates: Theoretical aspects","author":"figueiredo","year":"0","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref26","first-page":"100?103","article-title":"Introduction to information retrieval","volume":"16","author":"manning","year":"2010","journal-title":"Natural Lang Eng"},{"article-title":"The ordered weighted $\\ell _1$ norm: Atomic formulation, projections, and algorithms","year":"2014","author":"zeng","key":"ref25"}],"container-title":["IEEE Transactions on Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/78\/9307529\/09262084.pdf?arnumber=9262084","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:50:35Z","timestamp":1652194235000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9262084\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/tsp.2020.3038480","relation":{},"ISSN":["1053-587X","1941-0476"],"issn-type":[{"type":"print","value":"1053-587X"},{"type":"electronic","value":"1941-0476"}],"subject":[],"published":{"date-parts":[[2021]]}}}