{"id":"https://openalex.org/W7164007364","doi":"https://doi.org/10.48550/arxiv.2606.09729","title":"Bayesian Probing on Graphs","display_name":"Bayesian Probing on Graphs","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164007364","doi":"https://doi.org/10.48550/arxiv.2606.09729"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09729","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"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.2606.09729","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138228285","display_name":"Anupam Gupta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gupta, Anupam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138207703","display_name":"Benjamin Moseley","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moseley, Benjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138279816","display_name":"Rudy Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Rudy","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/T12072","display_name":"Machine Learning and Algorithms","score":0.40790000557899475,"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"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.40790000557899475,"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/T12288","display_name":"Optimization and Search Problems","score":0.10939999669790268,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.06279999762773514,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/knapsack-problem","display_name":"Knapsack problem","score":0.8011999726295471},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6330000162124634},{"id":"https://openalex.org/keywords/bayesian-statistics","display_name":"Bayesian statistics","score":0.4657999873161316},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.4246000051498413},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4101000130176544},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.38429999351501465},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.3808000087738037},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.37790000438690186}],"concepts":[{"id":"https://openalex.org/C113138325","wikidata":"https://www.wikidata.org/wiki/Q864457","display_name":"Knapsack problem","level":2,"score":0.8011999726295471},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6330000162124634},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.4657999873161316},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4480000138282776},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.4246000051498413},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41940000653266907},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4101000130176544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40540000796318054},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.397599995136261},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.37790000438690186},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3544999957084656},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C149569020","wikidata":"https://www.wikidata.org/wiki/Q25098598","display_name":"Bayesian average","level":5,"score":0.33809998631477356},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.334199994802475},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.32409998774528503},{"id":"https://openalex.org/C127043819","wikidata":"https://www.wikidata.org/wiki/Q16243608","display_name":"Bayesian programming","level":5,"score":0.28119999170303345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2799000144004822},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09729","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.09729","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09729","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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],"introduce":[1],"a":[2,32,67,70,86,116,125,145,157,166,218],"stochastic":[3,103,106],"probing":[4,107,150],"problem":[5,127,180],"with":[6,31,181],"correlated":[7],"items.":[8,112,155],"In":[9,134,194],"our":[10],"model,":[11],"which":[12],"we":[13,162,197],"call":[14],"Bayesian":[15,71,100,121,135,167,203,214],"Probing,":[16],"the":[17,42,61,74,91,97,120,130,138,171,208],"correlations":[18,110],"are":[19,56,185],"modeled":[20],"by":[21,108,148],"an":[22,39,45],"underlying":[23],"graph":[24],"$G$.":[25],"Each":[26,35],"vertex":[27],"is":[28,79,140],"independently":[29],"active":[30],"known":[33],"probability.":[34],"item":[36],"corresponds":[37],"to":[38,80,85,89,141,164,169],"edge":[40,46],"in":[41,129,144],"graph.":[43],"Probing":[44,101],"has":[47],"some":[48,51],"cost,":[49],"gives":[50,115],"reward":[52,94],"if":[53],"both":[54],"endpoints":[55],"active,":[57],"and":[58,105],"also":[59],"reveals":[60],"state":[62],"of":[63,173,220],"its":[64],"endpoints.":[65],"Hence":[66],"probe":[68,82],"induces":[69],"update":[72],"on":[73,189],"remaining":[75],"edges.":[76,99],"The":[77],"goal":[78,139],"adaptively":[81,149],"items/edges":[83],"subject":[84],"knapsack":[87,104],"constraint":[88],"maximize":[90,170],"expected":[92],"total":[93],"obtained":[95],"from":[96],"probed":[98],"generalizes":[102],"allowing":[109],"between":[111],"Moreover,":[113],"it":[114],"tractable":[117],"model":[118],"for":[119,202,213,217],"Active":[122,136,215],"Search":[123],"problem,":[124],"popular":[126],"considered":[128],"machine":[131],"learning":[132],"community.":[133],"Search,":[137,216],"find":[142],"items":[143,175],"particular":[146],"class":[147,219],"at":[151],"most,":[152],"say":[153],"$k$,":[154],"Given":[156],"prior":[158,222],"distribution":[159],"over":[160],"items,":[161],"want":[163],"compute":[165],"policy":[168],"number":[172],"such":[174],"found.":[176],"For":[177],"this":[178,195],"general":[179],"arbitrary":[182],"priors,":[183],"there":[184],"strong":[186],"lower":[187],"bounds":[188],"efficiently":[190],"computing":[191],"good":[192],"policies.":[193],"paper,":[196],"design":[198],"efficient":[199,210],"approximation":[200,211],"algorithms":[201,212],"Probing.":[204],"These":[205],"results":[206],"give":[207],"first":[209],"practically-relevant":[221],"distributions.":[223]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}