{"id":"https://openalex.org/W7164893790","doi":"https://doi.org/10.48550/arxiv.2606.16073","title":"Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels","display_name":"Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164893790","doi":"https://doi.org/10.48550/arxiv.2606.16073"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16073","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16073","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.2606.16073","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137398346","display_name":"Kirill Korolev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Korolev, Kirill","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128020864","display_name":"Nikita Morozov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Morozov, Nikita","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138722604","display_name":"Stepan Pavlenko","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pavlenko, Stepan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136442769","display_name":"Esmeralda S. Whitammer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Whitammer, Esmeralda S.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138698433","display_name":"Sergey Samsonov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Samsonov, Sergey","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":0.4733999967575073,"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":0.4733999967575073,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.257999986410141,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.07259999960660934,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6607000231742859},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5774000287055969},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5296000242233276},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5112000107765198},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45719999074935913},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4487000107765198},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43369999527931213},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4187000095844269},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.41530001163482666},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4108999967575073}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6607000231742859},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5774000287055969},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5479000210762024},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5296000242233276},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5220999717712402},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5112000107765198},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45719999074935913},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4487000107765198},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.41530001163482666},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4047999978065491},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.40299999713897705},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.39629998803138733},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.3716999888420105},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.36899998784065247},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3580999970436096},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32440000772476196},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.3176000118255615},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30640000104904175},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2849999964237213},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C86426650","wikidata":"https://www.wikidata.org/wiki/Q7452504","display_name":"Sequential estimation","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.25270000100135803},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.2522999942302704},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16073","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16073","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.2606.16073","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16073","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Sampling":[0],"from":[1,29],"complex,":[2],"unnormalized":[3],"probability":[4],"densities":[5,127],"is":[6],"a":[7,49,57,84,88,112],"fundamental":[8],"challenge":[9],"in":[10,119],"Bayesian":[11],"inference":[12],"and":[13,32,91,102,110,141],"probabilistic":[14],"modeling.":[15],"While":[16],"Markov":[17],"chain":[18],"Monte":[19],"Carlo":[20],"(MCMC)":[21],"methods":[22],"provide":[23],"asymptotic":[24],"guarantees,":[25],"they":[26],"often":[27],"suffer":[28],"slow":[30],"mixing":[31,142],"high":[33],"computational":[34],"costs":[35],"due":[36],"to":[37,81,116,144],"fixed":[38],"or":[39],"manually":[40],"tuned":[41],"trajectory":[42,54,85,135],"lengths.":[43],"In":[44],"this":[45],"work,":[46],"we":[47,76],"propose":[48],"novel":[50],"framework":[51],"that":[52,129],"treats":[53],"termination":[55],"as":[56],"learnable":[58],"component":[59],"of":[60,70],"the":[61,68,97,103],"sampling":[62],"dynamics.":[63],"By":[64],"framing":[65],"MCMC":[66,146],"within":[67],"theory":[69],"non-acyclic":[71],"generative":[72],"flow":[73],"networks":[74],"(GFlowNets),":[75],"train":[77],"state-dependent":[78],"neural":[79],"classifiers":[80,101],"decide":[82],"when":[83],"has":[86],"reached":[87],"high-density":[89],"region":[90],"should":[92],"terminate.":[93],"We":[94],"theoretically":[95],"establish":[96],"connection":[98],"between":[99],"optimal":[100],"target":[104],"density":[105],"via":[106],"detailed":[107],"balance":[108],"conditions":[109],"introduce":[111],"multilevel":[113],"training":[114],"scheme":[115],"facilitate":[117],"exploration":[118],"complex":[120],"geometries.":[121],"Experimental":[122],"results":[123],"across":[124],"various":[125],"benchmark":[126],"demonstrate":[128],"our":[130],"approach":[131],"significantly":[132],"reduces":[133],"average":[134],"lengths":[136],"while":[137],"improving":[138],"mode":[139],"coverage":[140],"compared":[143],"standard":[145],"baselines.":[147]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}
