{"id":"https://openalex.org/W7164888525","doi":"https://doi.org/10.48550/arxiv.2606.16790","title":"Decision-Weighted Flow Matching for Contextual Stochastic Optimization","display_name":"Decision-Weighted Flow Matching for Contextual Stochastic Optimization","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164888525","doi":"https://doi.org/10.48550/arxiv.2606.16790"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16790","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16790","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.2606.16790","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008507483","display_name":"Jize Xie","orcid":"https://orcid.org/0000-0001-9702-5025"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Jize","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113485823","display_name":"H. Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Haomiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138719104","display_name":"Qiang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Qiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138721419","display_name":"Xiu Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Xiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138697697","display_name":"Yi Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yi","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/T11413","display_name":"Risk and Portfolio Optimization","score":0.35100001096725464,"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"}},"topics":[{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.35100001096725464,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.3018999993801117,"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":0.07440000027418137,"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/regret","display_name":"Regret","score":0.8007000088691711},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6894000172615051},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.4982999861240387},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.48030000925064087},{"id":"https://openalex.org/keywords/stochastic-optimization","display_name":"Stochastic optimization","score":0.4345000088214874},{"id":"https://openalex.org/keywords/simplicity","display_name":"Simplicity","score":0.3928000032901764},{"id":"https://openalex.org/keywords/optimal-matching","display_name":"Optimal matching","score":0.3693999946117401},{"id":"https://openalex.org/keywords/ideal","display_name":"Ideal (ethics)","score":0.3434999883174896}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.8007000088691711},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6894000172615051},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.603600025177002},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4814000129699707},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.48030000925064087},{"id":"https://openalex.org/C194387892","wikidata":"https://www.wikidata.org/wiki/Q1747770","display_name":"Stochastic optimization","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40049999952316284},{"id":"https://openalex.org/C2776372474","wikidata":"https://www.wikidata.org/wiki/Q508291","display_name":"Simplicity","level":2,"score":0.3928000032901764},{"id":"https://openalex.org/C123853557","wikidata":"https://www.wikidata.org/wiki/Q7098946","display_name":"Optimal matching","level":3,"score":0.3693999946117401},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C137631369","wikidata":"https://www.wikidata.org/wiki/Q7617831","display_name":"Stochastic programming","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3221000134944916},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C115527620","wikidata":"https://www.wikidata.org/wiki/Q769909","display_name":"Nonlinear programming","level":3,"score":0.28290000557899475},{"id":"https://openalex.org/C127491075","wikidata":"https://www.wikidata.org/wiki/Q7617825","display_name":"Stochastic modelling","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C84839998","wikidata":"https://www.wikidata.org/wiki/Q5249245","display_name":"Decision rule","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.251800000667572},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16790","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16790","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.2606.16790","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16790","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":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6525618433952332}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Conditional":[0],"generative":[1],"models":[2],"are":[3],"increasingly":[4],"used":[5],"as":[6],"scenario":[7],"generators":[8],"for":[9],"stochastic":[10,126],"optimization,":[11],"but":[12],"standard":[13,72,143],"training":[14,65],"objectives":[15,111],"emphasize":[16],"uniform":[17],"distributional":[18],"fit":[19],"rather":[20],"than":[21],"the":[22,54,69,118],"downstream":[23,87,140],"decisions":[24],"induced":[25],"by":[26],"generated":[27],"scenarios.":[28],"This":[29],"creates":[30],"an":[31,99,104],"objective":[32,79],"mismatch:":[33],"errors":[34,47],"in":[35,48],"statistically":[36],"common":[37],"regions":[38,50],"may":[39],"have":[40],"little":[41],"effect":[42],"on":[43,122],"decision":[44,96],"regret,":[45],"whereas":[46],"decision-sensitive":[49,81],"can":[51],"substantially":[52],"change":[53],"optimal":[55],"action.":[56],"We":[57],"propose":[58],"Decision-Weighted":[59],"Flow":[60],"Matching":[61],"(DW-FM),":[62],"a":[63,94],"regret-aligned":[64,106],"framework":[66],"that":[67],"preserves":[68],"simplicity":[70],"of":[71,120],"flow":[73],"matching":[74],"while":[75],"reweighting":[76],"its":[77],"velocity-regression":[78],"using":[80],"endpoint":[82],"information.":[83],"Theoretically,":[84],"we":[85,116],"connect":[86],"regret":[88,113,141],"to":[89],"pathwise":[90],"velocity":[91],"mismatch":[92],"through":[93],"loss-induced":[95],"discrepancy":[97],"and":[98,108,134],"adjoint":[100],"transport":[101],"argument,":[102],"yielding":[103],"ideal":[105],"surrogate":[107],"practical":[109],"endpoint-weighted":[110],"with":[112],"guarantees.":[114],"Empirically,":[115],"demonstrate":[117],"effectiveness":[119],"DW-FM":[121,138],"three":[123],"CVaR-based":[124],"contextual":[125],"optimization":[127],"benchmarks":[128],"spanning":[129],"synthetic":[130],"portfolio,":[131],"semi-real":[132],"financial,":[133],"traffic-CVaR":[135],"tasks,":[136],"where":[137],"improves":[139],"over":[142],"baselines.":[144]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}