{"id":"https://openalex.org/W7160944833","doi":"https://doi.org/10.48550/arxiv.2605.10684","title":"Is Data Shapley Not Better than Random in Data Selection? Ask NASH","display_name":"Is Data Shapley Not Better than Random in Data Selection? Ask NASH","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160944833","doi":"https://doi.org/10.48550/arxiv.2605.10684"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10684","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.10684","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135928505","display_name":"Xiao Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135930104","display_name":"Jue Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fan, Jue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085515319","display_name":"Rachael Hwee Ling Sim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sim, Rachael Hwee Ling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135945795","display_name":"Zixuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135947724","display_name":"Nancy F. Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Nancy F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135965795","display_name":"Bryan Kian Hsiang Low","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Low, Bryan Kian Hsiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"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":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":0.6888999938964844,"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":0.6888999938964844,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.1136000007390976,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.02160000056028366,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/selection","display_name":"Selection (genetic algorithm)","score":0.5752999782562256},{"id":"https://openalex.org/keywords/nash-equilibrium","display_name":"Nash equilibrium","score":0.5735999941825867},{"id":"https://openalex.org/keywords/ask-price","display_name":"Ask price","score":0.5047000050544739},{"id":"https://openalex.org/keywords/best-response","display_name":"Best response","score":0.4884999990463257},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.47920000553131104},{"id":"https://openalex.org/keywords/shapley-value","display_name":"Shapley value","score":0.4453999996185303}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5799999833106995},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5752999782562256},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.5735999941825867},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.5047000050544739},{"id":"https://openalex.org/C32407928","wikidata":"https://www.wikidata.org/wiki/Q2733833","display_name":"Best response","level":3,"score":0.4884999990463257},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.47920000553131104},{"id":"https://openalex.org/C199022921","wikidata":"https://www.wikidata.org/wiki/Q240046","display_name":"Shapley value","level":3,"score":0.4453999996185303},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3880000114440918},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38600000739097595},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3140999972820282},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10684","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.10684","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10684","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Data":[0,25,45,73],"selection":[1,100,147],"studies":[2],"the":[3,19,34,64,111,142],"problem":[4],"of":[5,9,21,39,105,144],"identifying":[6],"high-quality":[7,87],"subsets":[8,55,88],"training":[10],"data.":[11],"While":[12],"some":[13],"existing":[14],"works":[15,42,76],"have":[16],"considered":[17],"selecting":[18],"subset":[20,38],"data":[22,99,125,146],"with":[23,148],"top-$m$":[24],"Shapley":[26,46,74],"or":[27],"other":[28,41],"semivalues":[29],"as":[30],"they":[31],"account":[32],"for":[33],"interaction":[35],"among":[36],"every":[37],"data,":[40],"argue":[43],"that":[44,56,130,138],"can":[47],"sometimes":[48],"perform":[49],"ineffectively":[50],"in":[51],"practice":[52],"and":[53,90,123],"select":[54,86],"are":[57],"no":[58],"better":[59],"than":[60],"random.":[61],"This":[62],"raises":[63],"questions:":[65],"(I)":[66,109],"Are":[67],"there":[68],"certain":[69],"\"Shapley-informative\"":[70],"settings":[71,84],"where":[72],"consistently":[75,89],"well?":[77],"(II)":[78,131],"Can":[79],"we":[80,95],"strategically":[81],"utilize":[82],"these":[83,133],"to":[85],"efficiently?":[91],"In":[92],"this":[93],"paper,":[94],"propose":[96],"a":[97],"novel":[98],"framework,":[101],"NASH":[102,139],"(Non-linear":[103],"Aggregation":[104],"SHapley-informative":[106],"components),":[107],"which":[108],"decomposes":[110],"target":[112],"utility":[113],"function":[114],"(e.g.,":[115],"validation":[116],"accuracy)":[117],"into":[118],"simpler,":[119],"Shapley-informative":[120],"component":[121],"functions,":[122],"selects":[124],"by":[126],"optimizing":[127],"an":[128],"objective":[129],"aggregates":[132],"components":[134],"non-linearly.":[135],"We":[136],"demonstrate":[137],"substantially":[140],"boosts":[141],"effectiveness":[143],"Shapley/semivalue-based":[145],"minimal":[149],"additional":[150],"runtime":[151],"cost.":[152]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}