{"id":"https://openalex.org/W4404342069","doi":"https://doi.org/10.48550/arxiv.2410.19131","title":"Maximum a Posteriori Inference for Factor Graphs via Benders' Decomposition","display_name":"Maximum a Posteriori Inference for Factor Graphs via Benders' Decomposition","publication_year":2024,"publication_date":"2024-10-24","ids":{"openalex":"https://openalex.org/W4404342069","doi":"https://doi.org/10.48550/arxiv.2410.19131"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2410.19131","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2410.19131","pdf_url":"https://arxiv.org/pdf/2410.19131","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2410.19131","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109843203","display_name":"Harsh Vardhan Dubey","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dubey, Harsh Vardhan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112482861","display_name":"Ji Ah Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Ji Ah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5067088371","display_name":"Patrick T. Flaherty","orcid":"https://orcid.org/0000-0001-7925-3227"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Flaherty, Patrick","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5109843203"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12292","display_name":"Graph Theory and Algorithms","score":0.9086999893188477,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9086999893188477,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/inference","display_name":"Inference","score":0.726855993270874},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.6083930134773254},{"id":"https://openalex.org/keywords/benders-decomposition","display_name":"Benders' decomposition","score":0.5879294872283936},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.5668204426765442},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5233870148658752},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5186754465103149},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4387870728969574},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.36066824197769165},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3413779139518738},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.24052593111991882},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2040920853614807},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18213114142417908},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.07897114753723145},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.05525502562522888},{"id":"https://openalex.org/keywords/epistemology","display_name":"Epistemology","score":0.03834441304206848}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.726855993270874},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.6083930134773254},{"id":"https://openalex.org/C27665512","wikidata":"https://www.wikidata.org/wiki/Q3042795","display_name":"Benders' decomposition","level":2,"score":0.5879294872283936},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.5668204426765442},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5233870148658752},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5186754465103149},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4387870728969574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36066824197769165},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3413779139518738},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.24052593111991882},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2040920853614807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18213114142417908},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.07897114753723145},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.05525502562522888},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.03834441304206848},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2410.19131","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2410.19131","pdf_url":"https://arxiv.org/pdf/2410.19131","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2410.19131","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2410.19131","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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2410.19131","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2410.19131","pdf_url":"https://arxiv.org/pdf/2410.19131","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3109196640","display_name":null,"funder_award_id":"1934846","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6203420807","display_name":null,"funder_award_id":"TRIPODS","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6755165505","display_name":null,"funder_award_id":"award","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404342069.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391298190","https://openalex.org/W4388311650","https://openalex.org/W5922282","https://openalex.org/W1974056099","https://openalex.org/W4245343541","https://openalex.org/W2150865841","https://openalex.org/W2963673069","https://openalex.org/W2747493828","https://openalex.org/W2138865713","https://openalex.org/W2169353922"],"abstract_inverted_index":{"Many":[0],"Bayesian":[1,106,161],"statistical":[2,73],"inference":[3,47,74,103],"problems":[4,135],"come":[5],"down":[6],"to":[7,37,85,88,113,148,182],"computing":[8],"a":[9,28,38,55,60,72,77,98,141,176],"maximum":[10,101],"a-posteriori":[11,102],"(MAP)":[12],"assignment":[13,24],"of":[14,63,68,127,144,196],"latent":[15,166],"variables.":[16],"Yet,":[17],"standard":[18,190],"methods":[19,188],"for":[20,100,159,189],"estimating":[21],"the":[22,33,69,82,91,114,125,160],"MAP":[23,46,156],"do":[25],"not":[26],"have":[27],"finite":[29],"time":[30],"guarantee":[31],"that":[32,45,109,172],"algorithm":[34,75],"has":[35,43],"converged":[36],"fixed":[39],"point.":[40],"Previous":[41],"research":[42],"found":[44],"can":[48],"be":[49],"represented":[50],"in":[51,90,104,133],"dual":[52,70,117],"form":[53],"as":[54,76,84,137],"linear":[56,78],"programming":[57,79],"problem":[58,118],"with":[59],"non-polynomial":[61],"number":[62,143],"constraints.":[64],"A":[65],"Lagrangian":[66],"relaxation":[67,92],"yields":[71],"problem.":[80],"However,":[81],"decision":[83],"which":[86],"constraints":[87,112,132],"remove":[89],"is":[93],"often":[94],"heuristic.":[95],"We":[96],"present":[97],"method":[99,123,174],"general":[105],"factor":[107],"models":[108],"sequentially":[110],"adds":[111],"fully":[115],"relaxed":[116],"using":[119],"Benders'":[120],"decomposition.":[121],"Our":[122],"enables":[124],"incorporation":[126],"expressive":[128],"integer":[129],"and":[130,140,165,185,193],"logical":[131],"clustering":[134],"such":[136],"must-link,":[138],"cannot-link,":[139],"minimum":[142],"whole":[145],"samples":[146],"allocated":[147],"each":[149],"cluster.":[150],"Using":[151],"this":[152],"approach,":[153],"we":[154],"derive":[155],"estimation":[157],"algorithms":[158],"Gaussian":[162],"mixture":[163],"model":[164],"Dirichlet":[167],"allocation.":[168],"Empirical":[169],"results":[170],"show":[171],"our":[173],"produces":[175],"higher":[177],"optimal":[178],"posterior":[179],"value":[180],"compared":[181],"Gibbs":[183],"sampling":[184],"variational":[186],"Bayes":[187],"data":[191],"sets":[192],"provides":[194],"certificate":[195],"convergence.":[197]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2024-11-14T00:00:00"}