{"id":"https://openalex.org/W7162648839","doi":"https://doi.org/10.48550/arxiv.2605.28198","title":"Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework","display_name":"Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162648839","doi":"https://doi.org/10.48550/arxiv.2605.28198"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28198","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.2605.28198","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137286983","display_name":"Junfeng Nie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nie, Junfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137306837","display_name":"Alvin Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Alvin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137246783","display_name":"Xiaohui Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xiaohui","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.3555999994277954,"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":0.3555999994277954,"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/T10028","display_name":"Topic Modeling","score":0.10769999772310257,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.1046999990940094,"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/robustness","display_name":"Robustness (evolution)","score":0.613099992275238},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5873000025749207},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.5400999784469604},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.47920000553131104},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.4733999967575073},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.37720000743865967},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.3619999885559082}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7436000108718872},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.613099992275238},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5873000025749207},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.5400999784469604},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.47920000553131104},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4733999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4449999928474426},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3310000002384186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3138999938964844},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2648000121116638},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2572000026702881},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28198","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.2605.28198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28198","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":{"Existing":[0],"approaches":[1],"for":[2,141],"synthetic":[3,150],"tabular":[4,71,108],"data":[5,21,151],"generation":[6],"are":[7,63,73,86],"based":[8],"on":[9,102],"either":[10],"purely":[11],"generative":[12],"models":[13],"or":[14],"LLMs,":[15],"both":[16],"of":[17],"which":[18],"struggle":[19],"with":[20,93],"heterogeneity,":[22],"logical":[23,57],"consistency,":[24],"rare-event":[25],"coverage,":[26],"and":[27,41,59,109,146],"robustness":[28],"in":[29,66,88,149],"low-data":[30],"regimes.":[31],"In":[32,52],"this":[33],"paper,":[34],"we":[35],"propose":[36],"a":[37,89],"hierarchical":[38,134],"hybrid":[39],"top-down":[40,54],"bottom-up":[42,68],"(H-TDBU)":[43],"framework":[44,101],"that":[45,115,133],"decouples":[46],"semantic":[47,128,144],"structures":[48],"from":[49,80],"stochastic":[50],"texture.":[51],"the":[53,67,100],"path,":[55,69],"structure-driven":[56],"constraints":[58],"cross-modal":[60],"alignment":[61],"rules":[62],"constructed,":[64],"while":[65,126],"lightweight":[70],"generators":[72],"used":[74],"to":[75],"learn":[76],"local":[77],"statistical":[78,147],"patterns":[79],"real":[81],"data.":[82,111],"The":[83],"two":[84],"paths":[85],"consolidated":[87],"unified":[90],"synthesis":[91,136],"engine":[92],"an":[94,138],"iterative":[95],"feedback":[96],"loop.":[97],"We":[98],"evaluate":[99],"weak":[103],"multimodal":[104],"financial":[105],"benchmarks":[106],"combining":[107,142],"sentiment-text":[110],"Experimental":[112],"results":[113,131],"show":[114],"our":[116],"H-TDBU":[117],"approach":[118],"improves":[119],"train-synthetic-test-real":[120],"performance":[121],"over":[122],"neural":[123],"baseline":[124],"methods":[125],"preserving":[127],"consistency.":[129],"Our":[130],"suggest":[132],"rule-guided":[135],"provides":[137],"effective":[139],"mechanism":[140],"controllability,":[143],"coherence,":[145],"fidelity":[148],"generation.":[152]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-29T00:00:00"}