{"id":"https://openalex.org/W7151025370","doi":"https://doi.org/10.48550/arxiv.2604.02644","title":"Conditional Sampling via Wasserstein Autoencoders and Triangular Transport","display_name":"Conditional Sampling via Wasserstein Autoencoders and Triangular Transport","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7151025370","doi":"https://doi.org/10.48550/arxiv.2604.02644"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02644","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02644","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.2604.02644","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102988972","display_name":"Mohammad Al-Jarrah","orcid":"https://orcid.org/0009-0006-0433-9230"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Al-Jarrah, Mohammad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133041937","display_name":"Michele Martino","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Martino, Michele","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133029970","display_name":"Marcus Yim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yim, Marcus","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012461935","display_name":"Bamdad Hosseini","orcid":"https://orcid.org/0000-0001-5053-6223"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hosseini, Bamdad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5035589000","display_name":"Amirhossein Taghvaei","orcid":"https://orcid.org/0000-0002-1536-892X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taghvaei, Amirhossein","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102988972"],"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":0.6049000024795532,"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":0.6049000024795532,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.08980000019073486,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.05220000073313713,"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/conditional-independence","display_name":"Conditional independence","score":0.7459999918937683},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7441999912261963},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.5512999892234802},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5130000114440918},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.4927999973297119},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.46630001068115234},{"id":"https://openalex.org/keywords/conditional-expectation","display_name":"Conditional expectation","score":0.44589999318122864},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.40230000019073486}],"concepts":[{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.7459999918937683},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7441999912261963},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.5512999892234802},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5038999915122986},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.4927999973297119},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.46630001068115234},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44699999690055847},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4196000099182129},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.40230000019073486},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3686000108718872},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3465999960899353},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3416000008583069},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C103982235","wikidata":"https://www.wikidata.org/wiki/Q7309594","display_name":"Regular conditional probability","level":4,"score":0.2985000014305115},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2892000079154968},{"id":"https://openalex.org/C21430997","wikidata":"https://www.wikidata.org/wiki/Q5159279","display_name":"Conditional variance","level":4,"score":0.2825999855995178},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02644","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02644","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.2604.02644","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02644","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":{"We":[0,48,72,88,105],"present":[1,90,106],"Conditional":[2],"Wasserstein":[3,30],"Autoencoders":[4],"(CWAEs),":[5],"a":[6,29,34,107],"framework":[7],"for":[8,69],"conditional":[9,70,83,141],"simulation":[10],"that":[11,50,57,93,112,114],"exploits":[12],"low-dimensional":[13,60],"structure":[14,61],"in":[15,122,134],"both":[16],"the":[17,20,45,51,64,100,127,137,140],"conditioned":[18],"and":[19,38],"conditioning":[21],"variables.":[22,47],"The":[23],"key":[24],"idea":[25],"is":[26,143],"to":[27,32,82,95,126],"modify":[28],"autoencoder":[31,56],"use":[33],"(block-)":[35],"triangular":[36],"decoder":[37,65],"impose":[39],"an":[40,55],"appropriate":[41],"independence":[42],"assumption":[43],"on":[44],"latent":[46],"show":[49],"resulting":[52],"model":[53],"gives":[54],"can":[58,66],"exploit":[59],"while":[62],"simultaneously":[63],"be":[67],"used":[68],"simulation.":[71],"explore":[73],"various":[74],"theoretical":[75],"properties":[76],"of":[77,102,109,139],"CWAEs,":[78],"including":[79],"their":[80],"connections":[81],"optimal":[84],"transport":[85],"(OT)":[86],"problems.":[87],"also":[89],"alternative":[91],"formulations":[92],"lead":[94],"three":[96],"architectural":[97],"variants":[98,118],"forming":[99],"foundation":[101],"our":[103,115],"algorithms.":[104],"series":[108],"numerical":[110],"experiments":[111],"demonstrate":[113],"different":[116],"CWAE":[117],"achieve":[119],"substantial":[120],"reductions":[121],"approximation":[123],"error":[124],"relative":[125],"low-rank":[128],"ensemble":[129],"Kalman":[130],"filter":[131],"(LREnKF),":[132],"particularly":[133],"problems":[135],"where":[136],"support":[138],"measures":[142],"truly":[144],"low-dimensional.":[145]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-04-07T00:00:00"}
