{"id":"https://openalex.org/W4387635174","doi":"https://doi.org/10.48550/arxiv.2310.08056","title":"Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation","display_name":"Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation","publication_year":2023,"publication_date":"2023-10-12","ids":{"openalex":"https://openalex.org/W4387635174","doi":"https://doi.org/10.48550/arxiv.2310.08056"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2310.08056","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.08056","pdf_url":"https://arxiv.org/pdf/2310.08056","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/2310.08056","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003553217","display_name":"Shreyas Havaldar","orcid":"https://orcid.org/0000-0001-8783-7791"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Havaldar, Shreyas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073512424","display_name":"Navodita Sharma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sharma, Navodita","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113022987","display_name":"Shubhi Sareen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sareen, Shubhi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021188761","display_name":"Karthikeyan Shanmugam","orcid":"https://orcid.org/0009-0008-2879-5868"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shanmugam, Karthikeyan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5084091166","display_name":"Aravindan Raghuveer","orcid":"https://orcid.org/0000-0001-5006-4385"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raghuveer, Aravindan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5003553217"],"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9661999940872192,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9661999940872192,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9361000061035156,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9041000008583069,"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/computer-science","display_name":"Computer science","score":0.6758822202682495},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6710976362228394},{"id":"https://openalex.org/keywords/bootstrapping","display_name":"Bootstrapping (finance)","score":0.6522402763366699},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.6232203245162964},{"id":"https://openalex.org/keywords/belief-propagation","display_name":"Belief propagation","score":0.5417945384979248},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5382788777351379},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5255305171012878},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.479512482881546},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4794663190841675},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46863484382629395},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.45803382992744446},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.45296669006347656},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32326996326446533},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2954631447792053},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2581864595413208},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.10557082295417786},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.08371216058731079}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6758822202682495},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6710976362228394},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.6522402763366699},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6232203245162964},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.5417945384979248},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5382788777351379},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5255305171012878},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.479512482881546},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4794663190841675},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46863484382629395},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.45803382992744446},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.45296669006347656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32326996326446533},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2954631447792053},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2581864595413208},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.10557082295417786},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.08371216058731079},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2310.08056","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.08056","pdf_url":"https://arxiv.org/pdf/2310.08056","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.2310.08056","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2310.08056","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:2310.08056","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.08056","pdf_url":"https://arxiv.org/pdf/2310.08056","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":[{"id":"https://metadata.un.org/sdg/10","score":0.4099999964237213,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387635174.pdf","grobid_xml":"https://content.openalex.org/works/W4387635174.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1534274833","https://openalex.org/W3117246195","https://openalex.org/W156620619","https://openalex.org/W2616249226","https://openalex.org/W2098233217","https://openalex.org/W2914363205","https://openalex.org/W2985746494","https://openalex.org/W2997844990","https://openalex.org/W1598221548","https://openalex.org/W2808284704"],"abstract_inverted_index":{"Learning":[0],"from":[1],"Label":[2],"Proportions":[3],"(LLP)":[4],"is":[5,27,174],"a":[6,55,79,140,144,172,228],"learning":[7,217],"problem":[8,61,197],"where":[9],"only":[10],"aggregate":[11],"level":[12,107],"labels":[13,85,102,135],"are":[14],"available":[15],"for":[16,59,139,164,192,222,227],"groups":[17],"of":[18],"instances,":[19],"called":[20],"bags,":[21],"during":[22],"training,":[23],"and":[24,47,103,204],"the":[25,30,34,37,69,92,105,118,126,133,151,157,165,169,177,193],"aim":[26],"to":[28,50,116,121,136,190,219],"get":[29],"best":[31],"performance":[32],"at":[33],"instance-level":[35],"on":[36,150,198],"test":[38],"data.":[39],"This":[40],"setting":[41],"arises":[42],"in":[43,74],"domains":[44],"like":[45],"advertising":[46],"medicine":[48],"due":[49,218],"privacy":[51],"considerations.":[52],"We":[53,110,206],"propose":[54],"novel":[56],"algorithmic":[57],"framework":[58],"this":[60],"that":[62,86,94,142],"iteratively":[63],"performs":[64],"two":[65,152],"main":[66],"steps.":[67],"For":[68],"first":[70],"step":[71,128],"(Pseudo":[72],"Labeling)":[73],"every":[75],"iteration,":[76,171],"we":[77,131,148],"define":[78],"Gibbs":[80,119],"distribution":[81,120],"over":[82],"binary":[83],"instance":[84],"incorporates":[87],"a)":[88],"covariate":[89],"information":[90],"through":[91],"constraint":[93],"instances":[95],"with":[96,210],"similar":[97,101],"covariates":[98,163],"should":[99],"have":[100],"b)":[104],"bag":[106,224],"aggregated":[108],"label.":[109],"then":[111],"use":[112,132],"Belief":[113,220],"Propagation":[114],"(BP)":[115],"marginalize":[117],"obtain":[122],"pseudo":[123,134,178],"labels.":[124,179],"In":[125,168],"second":[127,158],"(Embedding":[129],"Refinement),":[130],"provide":[137],"supervision":[138],"learner":[141],"yields":[143],"better":[145],"embedding.":[146],"Further,":[147],"iterate":[149],"steps":[153],"again":[154],"by":[155],"using":[156,176],"step's":[159],"embeddings":[160],"as":[161],"new":[162],"next":[166],"iteration.":[167],"final":[170],"classifier":[173],"trained":[175],"Our":[180],"algorithm":[181],"displays":[182],"strong":[183],"gains":[184],"against":[185],"several":[186],"SOTA":[187],"baselines":[188],"(up":[189],"15%)":[191],"LLP":[194],"Binary":[195],"Classification":[196],"various":[199],"dataset":[200],"types":[201],"-":[202],"tabular":[203],"Image.":[205],"achieve":[207],"these":[208],"improvements":[209],"minimal":[211],"computational":[212],"overhead":[213],"above":[214],"standard":[215],"supervised":[216],"Propagation,":[221],"large":[223],"sizes,":[225],"even":[226],"million":[229],"samples.":[230]},"counts_by_year":[],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}
