{"id":"https://openalex.org/W4407423832","doi":"https://doi.org/10.48550/arxiv.2502.06999","title":"Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models","display_name":"Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models","publication_year":2025,"publication_date":"2025-02-10","ids":{"openalex":"https://openalex.org/W4407423832","doi":"https://doi.org/10.48550/arxiv.2502.06999"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2502.06999","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.06999","pdf_url":"https://arxiv.org/pdf/2502.06999","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2502.06999","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012406406","display_name":"Siddarth Venkatraman","orcid":"https://orcid.org/0000-0002-3607-2781"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Venkatraman, Siddarth","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102482758","display_name":"Mohsin Hasan","orcid":"https://orcid.org/0009-0006-6013-3712"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hasan, Mohsin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343642","display_name":"Minsu Kim","orcid":"https://orcid.org/0000-0003-4472-0926"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Minsu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090728849","display_name":"Luca Scimeca","orcid":"https://orcid.org/0000-0002-2821-0072"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Scimeca, Luca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023066991","display_name":"Marcin Sendera","orcid":"https://orcid.org/0000-0002-8741-6919"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sendera, Marcin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086198262","display_name":"Yoshua Bengio","orcid":"https://orcid.org/0000-0002-9322-3515"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bengio, Yoshua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045351810","display_name":"Glen Berseth","orcid":"https://orcid.org/0000-0001-7351-8028"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Berseth, Glen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5068089852","display_name":"Nikolay Malkin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Malkin, Nikolay","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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9804999828338623,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9804999828338623,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7528569102287292},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6672058701515198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5431597828865051},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4982798099517822},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.4673939645290375},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4440476894378662},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.40151247382164},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3665481209754944},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2730048596858978},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08098152279853821},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.06225624680519104}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7528569102287292},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6672058701515198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5431597828865051},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4982798099517822},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.4673939645290375},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4440476894378662},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.40151247382164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3665481209754944},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2730048596858978},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08098152279853821},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.06225624680519104},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2502.06999","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.06999","pdf_url":"https://arxiv.org/pdf/2502.06999","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ruj.uj.edu.pl:item/567078","is_oa":true,"landing_page_url":"https://ruj.uj.edu.pl/handle/item/567078","pdf_url":null,"source":{"id":"https://openalex.org/S4306401249","display_name":"Jagiellonian University Repository (Jagiellonian University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I126596746","host_organization_name":"Jagiellonian University","host_organization_lineage":["https://openalex.org/I126596746"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"doi:10.48550/arxiv.2502.06999","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2502.06999","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":"pmh:oai:arXiv.org:2502.06999","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.06999","pdf_url":"https://arxiv.org/pdf/2502.06999","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4391584540","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4395044357","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W2087346071","https://openalex.org/W2967848559"],"abstract_inverted_index":{"Any":[0],"well-behaved":[1],"generative":[2],"model":[3,27],"over":[4],"a":[5,12,26,29,49,61,90],"variable":[6,21,40,68],"$\\mathbf{x}$":[7],"can":[8],"be":[9],"expressed":[10],"as":[11],"deterministic":[13],"transformation":[14],"of":[15,37,52,79],"an":[16,66],"exogenous":[17],"('outsourced')":[18],"Gaussian":[19],"noise":[20,94,132],"$\\mathbf{z}$:":[22],"$\\mathbf{x}=f_\u03b8(\\mathbf{z})$.":[23],"In":[24],"such":[25,82],"(\\eg,":[28],"VAE,":[30],"GAN,":[31,154],"or":[32],"continuous-time":[33],"flow-based":[34,157],"model),":[35],"sampling":[36,47,80,151,171],"the":[38,53,77,93,109,117,120,129,167],"target":[39],"$\\mathbf{x}":[41],"\\sim":[42],"p_\u03b8(\\mathbf{x})$":[43],"is":[44,60,70,134],"straightforward,":[45],"but":[46],"from":[48,81],"posterior":[50,83,118,130],"distribution":[51,91],"form":[54],"$p(\\mathbf{x}\\mid\\mathbf{y})":[55],"\\propto":[56],"p_\u03b8(\\mathbf{x})r(\\mathbf{x},\\mathbf{y})$,":[57],"where":[58],"$r$":[59],"constraint":[62],"function":[63],"depending":[64],"on":[65],"auxiliary":[67],"$\\mathbf{y}$,":[69],"generally":[71],"intractable.":[72],"We":[73,165],"propose":[74],"to":[75,106,116],"amortize":[76],"cost":[78],"distributions":[84],"with":[85,161,175,185],"diffusion":[86,98,170],"models":[87,126],"that":[88,108],"sample":[89],"in":[92,119,131,137,172],"space":[95,122,133],"($\\mathbf{z}$).":[96],"These":[97],"samplers":[99],"are":[100,113],"trained":[101],"by":[102],"reinforcement":[103,183],"learning":[104,184],"algorithms":[105],"enforce":[107],"transformed":[110],"samples":[111],"$f_\u03b8(\\mathbf{z})$":[112],"distributed":[114],"according":[115],"data":[121,138],"($\\mathbf{x}$).":[123],"For":[124],"many":[125],"and":[127,156,188],"constraints,":[128],"smoother":[135],"than":[136],"space,":[139],"making":[140],"it":[141],"more":[142],"suitable":[143],"for":[144],"amortized":[145],"inference.":[146],"Our":[147],"method":[148],"enables":[149],"conditional":[150,180],"under":[152],"unconditional":[153],"(H)VAE,":[155],"priors,":[158],"comparing":[159],"favorably":[160],"other":[162],"inference":[163],"methods.":[164],"demonstrate":[166],"proposed":[168],"outsourced":[169],"several":[173],"experiments":[174],"large":[176],"pretrained":[177],"prior":[178],"models:":[179],"image":[181],"generation,":[182],"human":[186],"feedback,":[187],"protein":[189],"structure":[190],"generation.":[191]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
