{"id":"https://openalex.org/W7138333196","doi":"https://doi.org/10.48550/arxiv.2603.14094","title":"Maximin Robust Bayesian Experimental Design","display_name":"Maximin Robust Bayesian Experimental Design","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138333196","doi":"https://doi.org/10.48550/arxiv.2603.14094"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14094","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14094","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.2603.14094","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080021372","display_name":"Hany Abdulsamad","orcid":"https://orcid.org/0000-0001-8683-8784"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdulsamad, Hany","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109786285","display_name":"Sahel Iqbal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Iqbal, Sahel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020493508","display_name":"Christian A. Naesseth","orcid":"https://orcid.org/0000-0002-2452-8374"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naesseth, Christian A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129649184","display_name":"Takuo Matsubara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matsubara, Takuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5090756119","display_name":"Adrien Corenflos","orcid":"https://orcid.org/0000-0002-8374-4659"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Corenflos, Adrien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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":0.513700008392334,"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":0.513700008392334,"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":0.07349999994039536,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.07240000367164612,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/minimax","display_name":"Minimax","score":0.6744999885559082},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5418999791145325},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.47589999437332153},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4731999933719635},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4620000123977661},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4528000056743622},{"id":"https://openalex.org/keywords/bayesian-experimental-design","display_name":"Bayesian experimental design","score":0.4271000027656555},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.4092999994754791},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.40779998898506165}],"concepts":[{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.6744999885559082},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5418999791145325},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5195000171661377},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5041999816894531},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.47589999437332153},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4731999933719635},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4620000123977661},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4528000056743622},{"id":"https://openalex.org/C99173435","wikidata":"https://www.wikidata.org/wiki/Q4874469","display_name":"Bayesian experimental design","level":5,"score":0.4271000027656555},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.40779998898506165},{"id":"https://openalex.org/C31531917","wikidata":"https://www.wikidata.org/wiki/Q915157","display_name":"Robust control","level":3,"score":0.39879998564720154},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3935000002384186},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.34459999203681946},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3019999861717224},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.29580000042915344},{"id":"https://openalex.org/C21031990","wikidata":"https://www.wikidata.org/wiki/Q355020","display_name":"Probability measure","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C124805900","wikidata":"https://www.wikidata.org/wiki/Q5159269","display_name":"Conditional mutual information","level":3,"score":0.2840000092983246},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27129998803138733},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.2669999897480011},{"id":"https://openalex.org/C186394612","wikidata":"https://www.wikidata.org/wiki/Q7098942","display_name":"Optimal design","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14094","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14094","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.2603.14094","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14094","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,30],"address":[1],"the":[2,13,20,47,51,57,61,70,101],"brittleness":[3],"of":[4,64,74],"Bayesian":[5],"experimental":[6],"design":[7,93],"under":[8],"model":[9],"misspecification":[10],"by":[11,40],"formulating":[12],"problem":[14],"as":[15,50,60],"a":[16,36,86],"max--min":[17],"game":[18],"between":[19],"experimenter":[21],"and":[22,55,72],"an":[23],"adversarial":[24],"nature":[25],"subject":[26],"to":[27,80,89],"information-theoretic":[28],"constraints.":[29],"demonstrate":[31],"that":[32,106],"this":[33],"approach":[34],"yields":[35],"robust":[37,52,102],"objective":[38],"governed":[39],"Sibson's":[41,82],"$\u03b1$-mutual":[42],"information":[43,66,104],"(MI),":[44],"which":[45],"identifies":[46],"$\u03b1$-tilted":[48],"posterior":[49],"belief":[53],"update":[54],"establishes":[56],"R\u00e9nyi":[58],"divergence":[59],"appropriate":[62],"measure":[63],"conditional":[65],"gain.":[67],"To":[68],"mitigate":[69],"bias":[71],"variance":[73],"nested":[75],"Monte":[76],"Carlo":[77],"estimators":[78],"needed":[79],"estimate":[81],"$\u03b1$-MI,":[83],"we":[84],"adopt":[85],"PAC-Bayes":[87],"framework":[88],"search":[90],"over":[91],"stochastic":[92],"policies,":[94],"yielding":[95],"rigorous":[96],"high-probability":[97],"lower":[98],"bounds":[99],"on":[100],"expected":[103],"gain":[105],"explicitly":[107],"control":[108],"finite-sample":[109],"error.":[110]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-18T00:00:00"}
