{"id":"https://openalex.org/W2594404444","doi":"https://doi.org/10.1007/s10994-019-05778-2","title":"Distributed Bayesian matrix factorization with limited communication","display_name":"Distributed Bayesian matrix factorization with limited communication","publication_year":2019,"publication_date":"2019-03-25","ids":{"openalex":"https://openalex.org/W2594404444","doi":"https://doi.org/10.1007/s10994-019-05778-2","mag":"2594404444"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-019-05778-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-019-05778-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-019-05778-2.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-019-05778-2.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiangju Qin","orcid":"https://orcid.org/0000-0003-1371-809X"},"institutions":[{"id":"https://openalex.org/I32943570","display_name":"Helsinki Institute for Information Technology","ror":"https://ror.org/05kph4940","country_code":"FI","type":"facility","lineage":["https://openalex.org/I133731052","https://openalex.org/I32943570","https://openalex.org/I9927081"]},{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Xiangju Qin","raw_affiliation_strings":["Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland"],"raw_orcid":"https://orcid.org/0000-0003-1371-809X","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland","institution_ids":["https://openalex.org/I32943570","https://openalex.org/I9927081"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Paul Blomstedt","orcid":null},"institutions":[{"id":"https://openalex.org/I32943570","display_name":"Helsinki Institute for Information Technology","ror":"https://ror.org/05kph4940","country_code":"FI","type":"facility","lineage":["https://openalex.org/I133731052","https://openalex.org/I32943570","https://openalex.org/I9927081"]},{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Paul Blomstedt","raw_affiliation_strings":["Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland","institution_ids":["https://openalex.org/I32943570","https://openalex.org/I9927081"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Eemeli Lepp\u00e4aho","orcid":null},"institutions":[{"id":"https://openalex.org/I32943570","display_name":"Helsinki Institute for Information Technology","ror":"https://ror.org/05kph4940","country_code":"FI","type":"facility","lineage":["https://openalex.org/I133731052","https://openalex.org/I32943570","https://openalex.org/I9927081"]},{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Eemeli Lepp\u00e4aho","raw_affiliation_strings":["Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland","institution_ids":["https://openalex.org/I32943570","https://openalex.org/I9927081"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Pekka Parviainen","orcid":null},"institutions":[{"id":"https://openalex.org/I32943570","display_name":"Helsinki Institute for Information Technology","ror":"https://ror.org/05kph4940","country_code":"FI","type":"facility","lineage":["https://openalex.org/I133731052","https://openalex.org/I32943570","https://openalex.org/I9927081"]},{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Pekka Parviainen","raw_affiliation_strings":["Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland","institution_ids":["https://openalex.org/I32943570","https://openalex.org/I9927081"]}]},{"author_position":"last","author":{"id":null,"display_name":"Samuel Kaski","orcid":null},"institutions":[{"id":"https://openalex.org/I32943570","display_name":"Helsinki Institute for Information Technology","ror":"https://ror.org/05kph4940","country_code":"FI","type":"facility","lineage":["https://openalex.org/I133731052","https://openalex.org/I32943570","https://openalex.org/I9927081"]},{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Samuel Kaski","raw_affiliation_strings":["Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Helsinki Institute for Information Technology (HIIT), Aalto University, Konemiehentie 2, Espoo, Finland","institution_ids":["https://openalex.org/I32943570","https://openalex.org/I9927081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32943570","https://openalex.org/I9927081"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.4769,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.63878198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"108","issue":"10","first_page":"1805","last_page":"1830"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11716","display_name":"Random Matrices and Applications","score":0.22439999878406525,"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/T11716","display_name":"Random Matrices and Applications","score":0.22439999878406525,"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/T12303","display_name":"Tensor decomposition and applications","score":0.09780000150203705,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.08760000020265579,"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/embarrassingly-parallel","display_name":"Embarrassingly parallel","score":0.9143000245094299},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5698999762535095},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5353999733924866},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5180000066757202},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4853000044822693},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.46540001034736633},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.4101000130176544},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.38359999656677246},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.3765999972820282}],"concepts":[{"id":"https://openalex.org/C126909462","wikidata":"https://www.wikidata.org/wiki/Q5369501","display_name":"Embarrassingly parallel","level":3,"score":0.9143000245094299},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6521000266075134},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5698999762535095},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5353999733924866},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5180000066757202},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4853000044822693},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.46540001034736633},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45210000872612},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.38359999656677246},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.3765999972820282},{"id":"https://openalex.org/C17349429","wikidata":"https://www.wikidata.org/wiki/Q1049914","display_name":"Matrix multiplication","level":3,"score":0.3725999891757965},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.36469998955726624},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3628999888896942},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.3043000102043152},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3001999855041504},{"id":"https://openalex.org/C2780595030","wikidata":"https://www.wikidata.org/wiki/Q3860309","display_name":"Multiplication (music)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C854659","wikidata":"https://www.wikidata.org/wiki/Q1859284","display_name":"Message passing","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C120373497","wikidata":"https://www.wikidata.org/wiki/Q1087987","display_name":"Parallel algorithm","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.26080000400543213},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C163834973","wikidata":"https://www.wikidata.org/wiki/Q2004891","display_name":"Numerical linear algebra","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25200000405311584}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1007/s10994-019-05778-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-019-05778-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-019-05778-2.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:openaire/bcf885c7-2573-4746-b182-f8500c263c18","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/bcf885c7-2573-4746-b182-f8500c263c18","pdf_url":null,"source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Qin, X, Blomstedt, P, Lepp\u00e4aho, E, Parviainen, P & Kaski, S 2019, 'Distributed Bayesian matrix factorization with limited communication', Machine Learning, vol. 108, no. 10, pp. 1805\u20131830. https://doi.org/10.1007/s10994-019-05778-2","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:arXiv.org:1703.00734","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1703.00734","pdf_url":"https://arxiv.org/pdf/1703.00734","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:aaltodoc.aalto.fi:123456789/102835","is_oa":true,"landing_page_url":"https://aaltodoc.aalto.fi/handle/123456789/102835","pdf_url":null,"source":{"id":"https://openalex.org/S4306401663","display_name":"Aaltodoc (Aalto University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I9927081","host_organization_name":"Aalto University","host_organization_lineage":["https://openalex.org/I9927081"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"publishedVersion"},{"id":"pmh:oai:aaltodoc.aalto.fi:123456789/37722","is_oa":true,"landing_page_url":"https://research.aalto.fi/en/publications/distributed-bayesian-matrix-factorization-with-limited-communication(8f440f9a-370a-48fd-8433-4318b7a976ba).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306401663","display_name":"Aaltodoc (Aalto University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I9927081","host_organization_name":"Aalto University","host_organization_lineage":["https://openalex.org/I9927081"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"publishedVersion"}],"best_oa_location":{"id":"doi:10.1007/s10994-019-05778-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-019-05778-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-019-05778-2.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4811104620","display_name":null,"funder_award_id":"671555","funder_id":"https://openalex.org/F4320335254","funder_display_name":"Horizon 2020"},{"id":"https://openalex.org/G5023028684","display_name":"Exascale Compound Activity Prediction Engine","funder_award_id":"671555","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G5498917553","display_name":null,"funder_award_id":"294238","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"},{"id":"https://openalex.org/G7842005466","display_name":null,"funder_award_id":"Horizon 2020","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G7966353708","display_name":null,"funder_award_id":"292334","funder_id":"https://openalex.org/F4320321108","funder_display_name":"Academy of Finland"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320321108","display_name":"Academy of Finland","ror":"https://ror.org/05k73zm37"},{"id":"https://openalex.org/F4320321394","display_name":"Aalto-Yliopisto","ror":"https://ror.org/020hwjq30"},{"id":"https://openalex.org/F4320335254","display_name":"Horizon 2020","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2594404444.pdf","grobid_xml":"https://content.openalex.org/works/W2594404444.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1466286227","https://openalex.org/W1563739387","https://openalex.org/W1975875968","https://openalex.org/W2029721016","https://openalex.org/W2054141820","https://openalex.org/W2085040216","https://openalex.org/W2101893470","https://openalex.org/W2121433185","https://openalex.org/W2133482456","https://openalex.org/W2145536610","https://openalex.org/W2157519573","https://openalex.org/W2171999519","https://openalex.org/W2305001871","https://openalex.org/W2397024933","https://openalex.org/W2560472701","https://openalex.org/W2607724735","https://openalex.org/W2617739603","https://openalex.org/W2621863315","https://openalex.org/W2963373052","https://openalex.org/W4205433696","https://openalex.org/W6617235532","https://openalex.org/W6678592595","https://openalex.org/W6917172014","https://openalex.org/W7070499600"],"related_works":[],"abstract_inverted_index":{"Bayesian":[0],"matrix":[1],"factorization":[2],"(BMF)":[3],"is":[4,31,122],"a":[5,78,97,126,139],"powerful":[6],"tool":[7],"for":[8,15,28,92],"producing":[9],"low-rank":[10],"representations":[11],"of":[12,73,81,99,128,132,166],"matrices":[13,30],"and":[14,19,33,38,116,155,163],"predicting":[16],"missing":[17],"values":[18],"providing":[20],"confidence":[21],"intervals.":[22],"Scaling":[23],"up":[24],"the":[25,45,54,70,82,88,135],"posterior":[26,84],"inference":[27,51],"massive-scale":[29],"challenging":[32],"requires":[34],"distributing":[35],"both":[36,114],"data":[37,64],"computation":[39],"over":[40,134],"many":[41],"workers,":[42],"making":[43],"communication":[44,55],"main":[46],"computational":[47],"bottleneck.":[48],"Embarrassingly":[49],"parallel":[50,94,150,164],"would":[52],"remove":[53],"needed,":[56],"by":[57],"using":[58],"completely":[59],"independent":[60],"computations":[61,95],"on":[62,113,142],"different":[63],"subsets,":[65],"but":[66],"it":[67],"suffers":[68],"from":[69],"inherent":[71],"unidentifiability":[72],"BMF":[74],"solutions.":[75],"We":[76],"introduce":[77],"hierarchical":[79],"decomposition":[80],"joint":[83],"distribution,":[85],"which":[86],"couples":[87],"subset":[89],"inferences,":[90],"allowing":[91],"embarrassingly":[93,149],"in":[96,153],"sequence":[98],"at":[100],"most":[101],"three":[102],"stages.":[103],"Using":[104],"an":[105,130],"efficient":[106],"approximate":[107],"implementation,":[108],"we":[109],"show":[110],"improvements":[111],"empirically":[112],"real":[115],"simulated":[117],"data.":[118],"Our":[119,145],"distributed":[120,162],"approach":[121],"able":[123],"to":[124,159],"achieve":[125],"speed-up":[127],"almost":[129],"order":[131],"magnitude":[133],"full":[136],"posterior,":[137],"with":[138],"negligible":[140],"effect":[141],"predictive":[143],"accuracy.":[144],"method":[146],"outperforms":[147],"state-of-the-art":[148],"MCMC":[151],"methods":[152],"accuracy,":[154],"achieves":[156],"results":[157],"competitive":[158],"other":[160],"available":[161],"implementations":[165],"BMF.":[167]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2017-03-16T00:00:00"}