{"id":"https://openalex.org/W4403577758","doi":"https://doi.org/10.1145/3627673.3680032","title":"LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions","display_name":"LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions","publication_year":2024,"publication_date":"2024-10-20","ids":{"openalex":"https://openalex.org/W4403577758","doi":"https://doi.org/10.1145/3627673.3680032"},"language":"en","primary_location":{"id":"doi:10.1145/3627673.3680032","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3680032","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3680032?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3680032?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114337359","display_name":"Anand Brahmbhatt","orcid":"https://orcid.org/0009-0005-6590-2376"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anand Brahmbhatt","raw_affiliation_strings":["Google Research, Bengaluru, India"],"raw_orcid":"https://orcid.org/0009-0005-6590-2376","affiliations":[{"raw_affiliation_string":"Google Research, Bengaluru, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093081370","display_name":"Mohith Pokala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohith Pokala","raw_affiliation_strings":["Google Research, Bengaluru, India"],"raw_orcid":"https://orcid.org/0009-0006-1992-5291","affiliations":[{"raw_affiliation_string":"Google Research, Bengaluru, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011759026","display_name":"Rishi Saket","orcid":"https://orcid.org/0000-0002-0848-6987"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rishi Saket","raw_affiliation_strings":["Google Research, Bengaluru, India"],"raw_orcid":"https://orcid.org/0000-0002-0848-6987","affiliations":[{"raw_affiliation_string":"Google Research, Bengaluru, India","institution_ids":[]}]},{"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":"Aravindan Raghuveer","raw_affiliation_strings":["Google Research, Bengaluru, India"],"raw_orcid":"https://orcid.org/0000-0001-5006-4385","affiliations":[{"raw_affiliation_string":"Google Research, Bengaluru, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3055,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.6578286,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"4374","last_page":"4381"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9771000146865845,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9771000146865845,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9524000287055969,"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"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9503999948501587,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7818167805671692},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6393077969551086},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6005322337150574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4380439221858978},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37062305212020874},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09491342306137085},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.09239023923873901}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7818167805671692},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6393077969551086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6005322337150574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4380439221858978},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37062305212020874},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09491342306137085},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.09239023923873901}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3627673.3680032","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3680032","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3680032?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3627673.3680032","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3627673.3680032","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3627673.3680032?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403577758.pdf","grobid_xml":"https://content.openalex.org/works/W4403577758.grobid-xml"},"referenced_works_count":20,"referenced_works":["https://openalex.org/W7903576","https://openalex.org/W1607038179","https://openalex.org/W2066446909","https://openalex.org/W2067624665","https://openalex.org/W2074694452","https://openalex.org/W2076618162","https://openalex.org/W2086209965","https://openalex.org/W2120742448","https://openalex.org/W2587084097","https://openalex.org/W2754977407","https://openalex.org/W2884402480","https://openalex.org/W2962766291","https://openalex.org/W3101170768","https://openalex.org/W3188372759","https://openalex.org/W4221153123","https://openalex.org/W4225285820","https://openalex.org/W4319653493","https://openalex.org/W4377009926","https://openalex.org/W4403577758","https://openalex.org/W6809846724"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"With":[0],"large":[1,129,168],"neural":[2],"models":[3],"becoming":[4,123],"increasingly":[5,124],"accurate":[6],"and":[7,13,23,52,102,135,154,189],"powerful,":[8],"they":[9],"have":[10],"raised":[11],"privacy":[12,32,120],"transparency":[14],"concerns":[15,121],"on":[16,37,96,115,138],"data":[17,20,49,69,74,117],"usage.":[18],"Therefore,":[19],"platforms,":[21],"regulations":[22],"user":[24],"expectations":[25],"are":[26,64],"rapidly":[27],"evolving":[28],"leading":[29],"to":[30,66,78,107,119],"enforcing":[31],"via":[33],"aggregation.":[34],"We":[35,142],"focus":[36],"the":[38,45,56,159,166,184,200,209,216],"use":[39],"case":[40],"of":[41,47,68,81,100,161,183,202,211],"online":[42],"advertising":[43],"where":[44,91],"emergence":[46],"aggregate":[48,116],"is":[50,94,122,127,165,215],"imminent":[51],"can":[53],"significantly":[54],"impact":[55],"multi-billion":[57],"dollar":[58],"industry.":[59],"In":[60],"aggregated":[61],"datasets,":[62,179],"labels":[63,109],"assigned":[65],"groups":[67,97],"points":[70],"rather":[71],"than":[72,195],"individual":[73,111],"points.":[75],"This":[76],"leads":[77],"a":[79,82,92,146],"formulation":[80],"weakly":[83],"supervised":[84],"task":[85],"-":[86,145],"Learning":[87],"from":[88],"Label":[89],"Proportions":[90],"model":[93],"trained":[95],"(a.k.a":[98],"bags)":[99],"instances":[101],"their":[103],"corresponding":[104],"label":[105],"proportions":[106],"predict":[108],"for":[110,132],"instances.":[112],"While":[113],"learning":[114],"due":[118],"popular":[125],"there":[126],"no":[128],"scale":[130,148,169],"benchmark":[131,149,172],"measuring":[133],"performance":[134,201],"guiding":[136],"improvements":[137],"this":[139,214],"important":[140],"task.":[141],"propose":[143],"LLP-Bench":[144,164],"web":[147],"with":[150,173],"~":[151],"70":[152],"datasets":[153,187],"45":[155],"million":[156],"datapoints.":[157],"To":[158,208],"best":[160,210],"our":[162,212],"knowledge,":[163,213],"first":[167,217],"tabular":[170],"LLP":[171],"an":[174],"extensive":[175],"diversity":[176],"in":[177,181,206,223],"constituent":[178],"realistic":[180],"terms":[182],"sponsored":[185],"search":[186],"used":[188],"aggregation":[190],"mechanisms":[191],"followed.":[192],"Through":[193],"more":[194],"3000":[196],"experiments":[197],"we":[198],"compare":[199],"9":[203],"SOTA":[204],"methods":[205],"detail.":[207],"study":[218],"that":[219],"compares":[220],"diverse":[221],"approaches":[222],"such":[224],"depth.":[225]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}