{"id":"https://openalex.org/W2103419665","doi":"https://doi.org/10.1145/1553374.1553465","title":"Sparse Gaussian graphical models with unknown block structure","display_name":"Sparse Gaussian graphical models with unknown block structure","publication_year":2009,"publication_date":"2009-06-14","ids":{"openalex":"https://openalex.org/W2103419665","doi":"https://doi.org/10.1145/1553374.1553465","mag":"2103419665"},"language":"en","primary_location":{"id":"doi:10.1145/1553374.1553465","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1553374.1553465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th Annual International Conference on Machine Learning","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023142437","display_name":"Benjamin M. Marlin","orcid":"https://orcid.org/0000-0002-2626-3410"},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Benjamin M. Marlin","raw_affiliation_strings":["University of British Columbia, Vancouver, Canada"],"affiliations":[{"raw_affiliation_string":"University of British Columbia, Vancouver, Canada","institution_ids":["https://openalex.org/I141945490"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103362817","display_name":"Kevin P. Murphy","orcid":null},"institutions":[{"id":"https://openalex.org/I141945490","display_name":"University of British Columbia","ror":"https://ror.org/03rmrcq20","country_code":"CA","type":"education","lineage":["https://openalex.org/I141945490"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kevin P. Murphy","raw_affiliation_strings":["University of British Columbia, Vancouver, Canada"],"affiliations":[{"raw_affiliation_string":"University of British Columbia, Vancouver, Canada","institution_ids":["https://openalex.org/I141945490"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5023142437"],"corresponding_institution_ids":["https://openalex.org/I141945490"],"apc_list":null,"apc_paid":null,"fwci":4.3174,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.94531007,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"705","last_page":"712"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.6202272176742554},{"id":"https://openalex.org/keywords/stochastic-block-model","display_name":"Stochastic block model","score":0.6113008260726929},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.6105881333351135},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5802261233329773},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5774176120758057},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.551223874092102},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5384703874588013},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.48418405652046204},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.47580891847610474},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.4429040253162384},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.43951615691185},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.43555378913879395},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40183037519454956},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37508416175842285},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3537747859954834},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.308925598859787},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.2534548044204712},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.14788836240768433},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.0906451940536499}],"concepts":[{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.6202272176742554},{"id":"https://openalex.org/C2779982251","wikidata":"https://www.wikidata.org/wiki/Q25053762","display_name":"Stochastic block model","level":3,"score":0.6113008260726929},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.6105881333351135},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5802261233329773},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5774176120758057},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.551223874092102},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5384703874588013},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48418405652046204},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.47580891847610474},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.4429040253162384},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.43951615691185},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.43555378913879395},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40183037519454956},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37508416175842285},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3537747859954834},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.308925598859787},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2534548044204712},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.14788836240768433},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0906451940536499},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1553374.1553465","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1553374.1553465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th Annual International Conference on Machine Learning","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.149.4362","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.4362","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.mcgill.ca/~icml2009/papers/284.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4300000071525574,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W171292237","https://openalex.org/W1504002966","https://openalex.org/W1506806321","https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1706044825","https://openalex.org/W1860991815","https://openalex.org/W1965752946","https://openalex.org/W1974105260","https://openalex.org/W1984567898","https://openalex.org/W2005661402","https://openalex.org/W2010824638","https://openalex.org/W2033222113","https://openalex.org/W2052386156","https://openalex.org/W2062125287","https://openalex.org/W2081746825","https://openalex.org/W2088088467","https://openalex.org/W2098084154","https://openalex.org/W2100236179","https://openalex.org/W2112814716","https://openalex.org/W2114682096","https://openalex.org/W2118163005","https://openalex.org/W2121454540","https://openalex.org/W2132555912","https://openalex.org/W2137683543","https://openalex.org/W2144799688","https://openalex.org/W2151128232","https://openalex.org/W2153699776","https://openalex.org/W2170744156","https://openalex.org/W2465607533","https://openalex.org/W2949581777","https://openalex.org/W2962897895","https://openalex.org/W3098045107","https://openalex.org/W3098834468","https://openalex.org/W3106319742","https://openalex.org/W4237471138","https://openalex.org/W4238253035","https://openalex.org/W4285719527","https://openalex.org/W6606997615","https://openalex.org/W6629510986","https://openalex.org/W6630001685"],"related_works":["https://openalex.org/W4320169517","https://openalex.org/W3006206211","https://openalex.org/W2109986081","https://openalex.org/W2417308975","https://openalex.org/W4297589944","https://openalex.org/W4368755698","https://openalex.org/W2964129930","https://openalex.org/W2843165098","https://openalex.org/W4200629115","https://openalex.org/W1861559499"],"abstract_inverted_index":{"Recent":[0],"work":[1],"has":[2,110],"shown":[3],"that":[4,47,66,78,107],"one":[5],"can":[6,129],"learn":[7],"the":[8,20,32,56,79,131],"structure":[9],"of":[10,133],"Gaussian":[11],"Graphical":[12],"Models":[13],"by":[14],"imposing":[15],"an":[16,97],"L1":[17,135],"penalty":[18],"on":[19,115],"precision":[21],"matrix,":[22],"and":[23,121,128],"then":[24],"using":[25,136],"efficient":[26,98],"convex":[27],"optimization":[28],"methods":[29],"to":[30,40,102,125],"find":[31],"penalized":[33],"maximum":[34],"likelihood":[35,114],"estimate.":[36],"This":[37,63],"is":[38,90],"similar":[39],"performing":[41],"MAP":[42],"estimation":[43],"with":[44],"a":[45,61,85],"prior":[46],"prefers":[48],"sparse":[49],"graphs.":[50],"In":[51],"this":[52],"paper,":[53],"we":[54,95],"use":[55],"stochastic":[57],"block":[58],"model":[59],"as":[60],"prior.":[62],"prefer":[64],"graphs":[65],"are":[67],"blockwise":[68],"sparse,":[69],"but":[70,94],"unlike":[71],"previous":[72],"work,":[73],"it":[74],"does":[75],"not":[76],"require":[77],"blocks":[80],"or":[81],"groups":[82],"be":[83],"specified":[84],"priori.":[86],"The":[87],"resulting":[88],"problem":[89],"no":[91],"longer":[92],"convex,":[93],"devise":[96],"variational":[99],"Bayes":[100],"algorithm":[101],"solve":[103],"it.":[104],"We":[105],"show":[106],"our":[108],"method":[109],"better":[111],"test":[112],"set":[113],"two":[116],"different":[117],"datasets":[118],"(motion":[119],"capture":[120],"gene":[122],"expression)":[123],"compared":[124],"independent":[126],"L1,":[127],"match":[130],"performance":[132],"group":[134],"manually":[137],"created":[138],"groups.":[139]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":7}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}