{"id":"https://openalex.org/W7150776069","doi":"https://doi.org/10.48550/arxiv.2604.02482","title":"SEDGE: Structural Extrapolated Data Generation","display_name":"SEDGE: Structural Extrapolated Data Generation","publication_year":2026,"publication_date":"2026-04-02","ids":{"openalex":"https://openalex.org/W7150776069","doi":"https://doi.org/10.48550/arxiv.2604.02482"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02482","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02482","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.02482","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133030036","display_name":"Kun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133036017","display_name":"Jiaqi Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Jiaqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133042162","display_name":"Yiqing Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yiqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067738651","display_name":"Ignavier Ng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ng, Ignavier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002034107","display_name":"Namrata Deka","orcid":"https://orcid.org/0009-0008-4701-7906"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deka, Namrata","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101844572","display_name":"Shaoan Xie","orcid":"https://orcid.org/0000-0003-1003-7459"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Shaoan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.3303999900817871,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.3303999900817871,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.10170000046491623,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0737999975681305,"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/identifiability","display_name":"Identifiability","score":0.785099983215332},{"id":"https://openalex.org/keywords/extrapolation","display_name":"Extrapolation","score":0.7141000032424927},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4724999964237213},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4327999949455261},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.3709999918937683},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.3474000096321106},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.31200000643730164}],"concepts":[{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.785099983215332},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.7141000032424927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6518999934196472},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5163999795913696},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4934999942779541},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4724999964237213},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.44920000433921814},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4327999949455261},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3474000096321106},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C100463513","wikidata":"https://www.wikidata.org/wiki/Q5227322","display_name":"Data model (GIS)","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C119247159","wikidata":"https://www.wikidata.org/wiki/Q1366192","display_name":"System identification","level":3,"score":0.25119999051094055},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02482","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02482","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":"doi:10.48550/arxiv.2604.02482","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02482","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":"article"},"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":{"This":[0],"paper":[1],"aims":[2],"to":[3,81,115],"address":[4],"the":[5,11,29,48,52,64,74,99,117,120],"challenge":[6],"of":[7,51,54,67,119],"data":[8,13,38,56,84,104],"generation":[9,110],"beyond":[10],"training":[12],"and":[14,105],"proposes":[15],"a":[16,88,112],"framework":[17],"for":[18],"Structural":[19],"Extrapolated":[20],"Data":[21],"GEneration":[22],"(SEDGE)":[23],"based":[24,86],"on":[25,28,87,102],"suitable":[26],"assumptions":[27],"underlying":[30],"data-generating":[31],"process.":[32],"We":[33,97],"provide":[34],"conditions":[35],"under":[36,57],"which":[37],"satisfying":[39],"novel":[40],"specifications":[41],"can":[42],"be":[43],"generated":[44],"reliably,":[45],"together":[46],"with":[47],"approximate":[49],"identifiability":[50],"distribution":[53,69],"such":[55,71],"certain":[58],"``conservative\"":[59],"assumptions,":[60],"as":[61,63,111],"well":[62],"inherent":[65],"non-identifiability":[66],"this":[68],"without":[70],"assumptions.":[72],"On":[73],"algorithmic":[75],"side,":[76],"we":[77],"develop":[78],"practical":[79],"methods":[80],"achieve":[82],"extrapolated":[83,108],"generation,":[85],"structure-informed":[89],"optimization":[90],"strategy":[91],"or":[92],"diffusion":[93],"posterior":[94],"sampling,":[95],"respectively.":[96],"verify":[98],"extrapolation":[100],"performance":[101],"synthetic":[103],"also":[106],"consider":[107],"image":[109],"real-world":[113],"scenario":[114],"illustrate":[116],"validity":[118],"proposed":[121],"framework.":[122]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-07T00:00:00"}
