{"id":"https://openalex.org/W7164941559","doi":"https://doi.org/10.48550/arxiv.2606.15115","title":"Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning","display_name":"Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning","publication_year":2026,"publication_date":"2026-06-13","ids":{"openalex":"https://openalex.org/W7164941559","doi":"https://doi.org/10.48550/arxiv.2606.15115"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.15115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15115","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":null,"license_id":null,"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.2606.15115","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021436608","display_name":"Y C Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yiyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138757845","display_name":"Yaolin Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Yaolin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100577275","display_name":"Xiang Xia","orcid":"https://orcid.org/0009-0004-4801-1830"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112964640","display_name":"Xin An","orcid":"https://orcid.org/0000-0002-0291-2711"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"An, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138711450","display_name":"Hanyi Si","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Si, Hanyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101345625","display_name":"Xiang Shu","orcid":"https://orcid.org/0009-0008-1383-1301"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shu, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073328301","display_name":"\u5085\u63da\u5fb7","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Yangde","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057509292","display_name":"\u7aa6\u4eae","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dou, Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138702244","display_name":"Hong Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Hong","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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9787999987602234,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9787999987602234,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T11413","display_name":"Risk and Portfolio Optimization","score":0.0031999999191612005,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.0020000000949949026,"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/pareto-principle","display_name":"Pareto principle","score":0.6080999970436096},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.579200029373169},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5288000106811523},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"Multi-objective optimization","score":0.5034999847412109},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.42590001225471497},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.3833000063896179},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.35530000925064087}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6531000137329102},{"id":"https://openalex.org/C137635306","wikidata":"https://www.wikidata.org/wiki/Q182667","display_name":"Pareto principle","level":2,"score":0.6080999970436096},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.579200029373169},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5288000106811523},{"id":"https://openalex.org/C68781425","wikidata":"https://www.wikidata.org/wiki/Q2052203","display_name":"Multi-objective optimization","level":2,"score":0.5034999847412109},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.49230000376701355},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39989998936653137},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38179999589920044},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.35530000925064087},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30309998989105225},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C2986314615","wikidata":"https://www.wikidata.org/wiki/Q36829","display_name":"Pareto optimal","level":3,"score":0.260699987411499}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.15115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15115","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.15115","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15115","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-objective":[0],"optimization":[1,12,29],"(MOO)":[2],"has":[3],"emerged":[4],"as":[5,41],"a":[6,33,123,161,194,200,217],"powerful":[7],"approach":[8],"to":[9,47,55,79,89,120,170,182],"solving":[10],"complex":[11],"problems":[13],"involving":[14],"multiple":[15],"objectives.":[16],"In":[17,37,159],"many":[18],"practical":[19],"scenarios,":[20],"function":[21],"evaluations":[22],"are":[23,102],"unavailable":[24],"or":[25],"prohibitively":[26],"expensive,":[27],"necessitating":[28],"solely":[30],"based":[31],"on":[32,96,239],"fixed":[34],"offline":[35,42,222],"dataset.":[36],"this":[38,110,210],"setting,":[39],"known":[40],"MOO,":[43],"the":[44,50,56,64,69,80,87,97,140,148,152,156,221,232,248,260],"goal":[45],"is":[46,72,168],"find":[48,121],"out":[49,122],"Pareto":[51,99,163,184],"set":[52,127,164,204],"without":[53],"access":[54],"true":[57,98],"objective":[58],"functions.":[59],"This":[60],"setting":[61,223],"suffers":[62],"from":[63],"out-of-distribution":[65],"(OOD)":[66],"issue,":[67,82],"where":[68],"surrogate":[70,83],"model":[71],"not":[73,94],"accurate":[74],"for":[75,220],"unseen":[76],"designs.":[77],"Due":[78],"OOD":[81,149],"errors":[84],"may":[85],"cause":[86],"optimizer":[88],"select":[90],"solutions":[91,145],"that":[92,138,198,224,245],"do":[93],"lie":[95],"front":[100,185],"and":[101,125,146,155,174,202,228,230,241,257],"biased":[103],"toward":[104],"its":[105],"extremes.":[106],"To":[107,187,208],"address":[108],"this,":[109],"paper":[111],"proposes":[112],"Diversity-driven":[113],"Offline":[114],"Multi-Objective":[115],"Optimization":[116],"(DOMOO),":[117],"which":[118],"aims":[119],"diverse":[124,183],"high-quality":[126],"of":[128,143,205,234],"solutions.":[129,158,207],"First,":[130],"DOMOO":[131,246],"incorporates":[132],"an":[133],"accumulative":[134],"risk":[135,142],"control":[136],"module":[137],"estimates":[139],"potential":[141],"candidate":[144],"alleviates":[147],"issue":[150],"between":[151],"training":[153],"data":[154],"generated":[157],"addition,":[160],"nested":[162],"learning":[165],"(PSL)":[166],"strategy":[167,197],"proposed":[169],"jointly":[171],"learn":[172],"preference":[173],"PSL":[175],"parameters,":[176],"then":[177],"optimize":[178],"them,":[179],"enabling":[180],"adaptation":[181],"geometries.":[186],"further":[188],"enhance":[189],"solution":[190],"quality,":[191],"we":[192,214],"design":[193],"diversity-driven":[195,211],"selection":[196,212],"extracts":[199],"representative":[201],"well-distributed":[203],"final":[206],"achieve":[209],"strategy,":[213],"propose":[215],"$\\text{IGD}_\\text{offline}$,":[216],"tailored":[218],"indicator":[219],"considers":[225],"both":[226,255],"diversity":[227,258],"convergence,":[229],"avoids":[231],"bias":[233],"hypervolume":[235],"indicator.":[236],"Extensive":[237],"experiments":[238],"synthetic":[240],"real-world":[242],"benchmarks":[243],"show":[244],"achieves":[247],"best":[249],"average":[250],"rank":[251],"across":[252],"tasks":[253],"in":[254],"convergence":[256],"among":[259],"compared":[261],"methods.":[262]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}