{"id":"https://openalex.org/W7165686432","doi":"https://doi.org/10.48550/arxiv.2606.23662","title":"Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives","display_name":"Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165686432","doi":"https://doi.org/10.48550/arxiv.2606.23662"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23662","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23662","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.2606.23662","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139204114","display_name":"Tom Rossa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rossa, Tom","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063065730","display_name":"Angus Phillips","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Phillips, Angus","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5078631467","display_name":"Tom Rainforth","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rainforth, Tom","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/T11798","display_name":"Optimal Experimental Design Methods","score":0.38440001010894775,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.38440001010894775,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.22519999742507935,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.03999999910593033,"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/downstream","display_name":"Downstream (manufacturing)","score":0.7949000000953674},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5902000069618225},{"id":"https://openalex.org/keywords/sequential-analysis","display_name":"Sequential analysis","score":0.4381999969482422},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.3889000117778778},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.37860000133514404},{"id":"https://openalex.org/keywords/decision-model","display_name":"Decision model","score":0.36399999260902405},{"id":"https://openalex.org/keywords/marginal-utility","display_name":"Marginal utility","score":0.3630000054836273},{"id":"https://openalex.org/keywords/bayesian-experimental-design","display_name":"Bayesian experimental design","score":0.33629998564720154},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.334199994802475}],"concepts":[{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.7949000000953674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6764000058174133},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5902000069618225},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.4381999969482422},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.37860000133514404},{"id":"https://openalex.org/C59594135","wikidata":"https://www.wikidata.org/wiki/Q5249242","display_name":"Decision model","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C138551086","wikidata":"https://www.wikidata.org/wiki/Q842271","display_name":"Marginal utility","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3386000096797943},{"id":"https://openalex.org/C99173435","wikidata":"https://www.wikidata.org/wiki/Q4874469","display_name":"Bayesian experimental design","level":5,"score":0.33629998564720154},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.33320000767707825},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.30979999899864197},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.3010999858379364},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2531000077724457},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.25290000438690186},{"id":"https://openalex.org/C186394612","wikidata":"https://www.wikidata.org/wiki/Q7098942","display_name":"Optimal design","level":2,"score":0.25290000438690186},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23662","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23662","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.2606.23662","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23662","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":[{"score":0.6449923515319824,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Bayesian":[0],"experimental":[1],"design":[2,105,164],"(BED)":[3],"has":[4],"traditionally":[5],"been":[6],"based":[7],"on":[8,70],"maximising":[9],"expected":[10,66],"uncertainty":[11],"reductions":[12],"from":[13,146],"prior":[14],"to":[15,27,34,39,102,119,144,166,181],"posterior.":[16],"A":[17],"major":[18],"shortfall":[19],"of":[20,43,64],"this":[21],"approach":[22,116],"is":[23,137],"that":[24,31,56,84,95],"it":[25],"leads":[26],"doubly":[28],"intractable":[29,93],"objectives":[30,94],"are":[32],"difficult":[33],"optimise,":[35],"while":[36,177],"customising":[37],"them":[38],"particular":[40],"downstream":[41,71,109,158,183],"tasks":[42,184],"interest":[44],"can":[45,58,88,96],"also":[46],"be":[47,60,89,97,167],"difficult.":[48],"Following":[49],"first":[50],"principles":[51],"decision":[52],"theory,":[53],"we":[54,81,117],"demonstrate":[55],"BED":[57],"alternatively":[59],"formulated":[61],"in":[62],"terms":[63],"an":[65,115],"future":[67],"loss":[68,159],"(EFL)":[69],"actions,":[72],"providing":[73,178],"a":[74,108],"simple":[75],"and":[76,107,136,153,155,172,185],"naturally":[77,138],"task-driven":[78],"framework.":[79],"Critically,":[80],"then":[82],"show":[83],"all":[85],"such":[86],"EFLs":[87],"rearranged":[90],"into":[91],"singly":[92],"jointly":[98],"optimised":[99],"with":[100],"respect":[101],"both":[103],"the":[104,126,142,147,157],"policy":[106,111],"action":[110],"using":[112],"stochastic":[113],"gradients,":[114],"refer":[118],"as":[120],"ACTION-BED.":[121],"This":[122],"formulation":[123],"further":[124],"sidesteps":[125],"need":[127],"for":[128],"any":[129],"explicit":[130],"posterior":[131],"or":[132],"marginal":[133],"likelihood":[134],"estimation":[135],"implicit,":[139],"requiring":[140],"only":[141],"ability":[143],"sample":[145],"joint":[148],"model":[149,151],"over":[150],"parameters":[152],"data,":[154],"evaluate":[156],"function.":[160],"It":[161],"thus":[162],"allows":[163],"policies":[165],"learned":[168],"more":[169],"effectively,":[170],"efficiently,":[171],"simply":[173],"than":[174],"existing":[175],"methods,":[176],"easy":[179],"customisation":[180],"different":[182],"losses.":[186]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}