{"id":"https://openalex.org/W4226282865","doi":"https://doi.org/10.48550/arxiv.2204.00296","title":"Scalable Semi-Modular Inference with Variational Meta-Posteriors","display_name":"Scalable Semi-Modular Inference with Variational Meta-Posteriors","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226282865","doi":"https://doi.org/10.48550/arxiv.2204.00296"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2204.00296","pdf_url":null,"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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2204.00296","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013900573","display_name":"Chris U. Carmona","orcid":"https://orcid.org/0000-0003-0224-4968"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Carmona, Chris U.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009428718","display_name":"Geoff K. Nicholls","orcid":"https://orcid.org/0000-0002-1595-9041"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicholls, Geoff K.","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.640014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":60,"max":70},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9662,"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"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9662,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9573,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9057,"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":[],"concepts":[{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.7765117},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.71664155},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.69592214},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6578959},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5951261},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.54128337},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48964316},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4655882},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43896323},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.41775042},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41513872},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.36972702},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.35599786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.21596214},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16936588},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2204.00296","pdf_url":null,"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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.00296","pdf_url":"http://arxiv.org/pdf/2204.00296","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2204.00296","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2204.00296","pdf_url":null,"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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W88131178","https://openalex.org/W4390489571","https://openalex.org/W4226115828","https://openalex.org/W3192946336","https://openalex.org/W2330406685","https://openalex.org/W2314430310","https://openalex.org/W2167234030","https://openalex.org/W2078267893","https://openalex.org/W1529069387","https://openalex.org/W140187403"],"abstract_inverted_index":{"The":[0],"Cut":[1,43,86],"posterior":[2,26,44],"and":[3,41,45,87,114],"related":[4,66],"Semi-Modular":[5],"Inference":[6],"are":[7,91],"Generalised":[8],"Bayes":[9],"methods":[10,82],"for":[11,83,111],"Modular":[12],"Bayesian":[13],"evidence":[14,98],"combination.":[15,99],"Analysis":[16],"is":[17,59,127],"broken":[18],"up":[19],"over":[20],"modular":[21],"sub-models":[22],"of":[23,97,104,122,138,148],"the":[24,42,54,68,85,94],"joint":[25],"distribution.":[27],"Model-misspecification":[28],"in":[29],"multi-modular":[30],"models":[31,123],"can":[32],"be":[33],"hard":[34],"to":[35,67,93],"fix":[36],"by":[37,61,142],"model":[38],"elaboration":[39],"alone":[40],"SMI":[46,88,139],"offer":[47],"a":[48,102,108,130,136,145],"way":[49],"round":[50],"this.":[51],"Information":[52],"entering":[53],"analysis":[55,121],"from":[56],"misspecified":[57],"modules":[58],"controlled":[60],"an":[62],"influence":[63],"parameter":[64],"$\\eta$":[65,143],"learning":[69],"rate.":[70],"This":[71,134],"paper":[72],"contains":[73],"two":[74],"substantial":[75],"new":[76,131],"methods.":[77],"First,":[78],"we":[79,118],"give":[80],"variational":[81,105,149],"approximating":[84],"posteriors":[89,106,140],"which":[90],"adapted":[92],"inferential":[95],"goals":[96],"We":[100],"parameterise":[101],"family":[103,137],"using":[107,129,144],"Normalising":[109],"Flow":[110],"accurate":[112],"approximation":[113],"end-to-end":[115],"training.":[116],"Secondly,":[117],"show":[119],"that":[120],"with":[124],"multiple":[125],"cuts":[126],"feasible":[128],"Variational":[132],"Meta-Posterior.":[133],"approximates":[135],"indexed":[141],"single":[146],"set":[147],"parameters.":[150]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4226282865","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-01-06T05:10:03.411476","created_date":"2022-05-05"}