{"id":"https://openalex.org/W4416251985","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227713","title":"Upper confidence bound multi-armed bandits for partially observed Hawkes processes","display_name":"Upper confidence bound multi-armed bandits for partially observed Hawkes processes","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251985","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227713"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227713","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227713","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083293836","display_name":"Wen-Hao Chiang","orcid":"https://orcid.org/0000-0003-2300-738X"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wen-Hao Chiang","raw_affiliation_strings":["Amazon Web Services"],"affiliations":[{"raw_affiliation_string":"Amazon Web Services","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080879125","display_name":"George Mohler","orcid":"https://orcid.org/0000-0003-4293-5106"},"institutions":[{"id":"https://openalex.org/I103531236","display_name":"Boston College","ror":"https://ror.org/02n2fzt79","country_code":"US","type":"education","lineage":["https://openalex.org/I103531236"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"George Mohler","raw_affiliation_strings":["Boston College"],"affiliations":[{"raw_affiliation_string":"Boston College","institution_ids":["https://openalex.org/I103531236"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083293836"],"corresponding_institution_ids":["https://openalex.org/I1311688040"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41939557,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.7544000148773193,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.7544000148773193,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.10679999738931656,"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/T11830","display_name":"Point processes and geometric inequalities","score":0.048700001090765,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/point-process","display_name":"Point process","score":0.7789000272750854},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.7480999827384949},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6676999926567078},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5551999807357788},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5507000088691711},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5467000007629395},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.5263000130653381},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5023000240325928}],"concepts":[{"id":"https://openalex.org/C88871306","wikidata":"https://www.wikidata.org/wiki/Q7208287","display_name":"Point process","level":2,"score":0.7789000272750854},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.7480999827384949},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6676999926567078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6632000207901001},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5551999807357788},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5507000088691711},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5023000240325928},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.39879998564720154},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29159998893737793},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C2779466056","wikidata":"https://www.wikidata.org/wiki/Q107630651","display_name":"Time point","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2590000033378601},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.25609999895095825},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227713","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227713","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W157259654","https://openalex.org/W2014482607","https://openalex.org/W2077902449","https://openalex.org/W2110005947","https://openalex.org/W2116067849","https://openalex.org/W2167409531","https://openalex.org/W2168405694","https://openalex.org/W2791530986","https://openalex.org/W2796776178","https://openalex.org/W2963559990","https://openalex.org/W3020984775","https://openalex.org/W3110803744","https://openalex.org/W3182350438","https://openalex.org/W3199583143","https://openalex.org/W3201103413","https://openalex.org/W3206927749","https://openalex.org/W4205621617","https://openalex.org/W4283816800","https://openalex.org/W4312919606","https://openalex.org/W4402351379","https://openalex.org/W4402351850"],"related_works":[],"abstract_inverted_index":{"We":[0,165,181],"consider":[1],"the":[2,22,78,81,99,119,137,144,157,160,175,222],"problem":[3],"of":[4,10,24,33,54,83,107,114,122,124,146,159,177,179],"estimating":[5],"and":[6,112,193,196,203,224],"ranking":[7],"a":[8,31,52,128,168],"set":[9],"self-excited":[11,60],"point":[12,94,101,110,163,218],"processes":[13,95,111,148],"when":[14,38],"an":[15,132],"action":[16],"must":[17],"be":[18,214],"taken":[19],"to":[20,51,72,96,140,149,216],"observe":[21,150],"events":[23,61,123],"each":[25,125],"process.":[26],"This":[27],"situation":[28],"arises":[29],"in":[30,42,143,151,191],"number":[32,121,176],"real-world":[34],"applications,":[35],"for":[36,91],"example,":[37],"crime":[39,188],"goes":[40],"unreported":[41],"some":[43],"regions,":[44],"or":[45],"COVID-19":[46],"cases":[47],"are":[48],"undetected":[49],"due":[50],"lack":[53],"testing":[55],"resources.":[56],"Often":[57],"times,":[58],"such":[59,93],"may":[62],"bear":[63],"implicit":[64],"causality.":[65],"Therefore,":[66],"we":[67,85,130],"start":[68],"with":[69,174],"Hawkes":[70,87],"Processes":[71],"model":[73,184,206],"how":[74],"one":[75],"event":[76,198],"triggers":[77],"other.":[79],"In":[80],"scenario":[82],"undersampling,":[84],"propose":[86],"Process":[88],"Multi-armed":[89],"Bandits":[90],"learning":[92],"quickly":[97],"learn":[98],"riskiest":[100],"processes,":[102],"while":[103],"carefully":[104],"balancing":[105],"exploitation":[106],"known":[108],"(observed)":[109],"exploration":[113],"unknown":[115],"processes.":[116,164],"By":[117],"considering":[118],"cumulative":[120],"process":[126,139,219],"as":[127],"reward,":[129],"derive":[131,167],"upper":[133],"confidence":[134],"bound":[135,170],"on":[136,156,185],"counting":[138],"inform":[141],"actions":[142],"form":[145],"which":[147],"upcoming":[152],"MAB":[153,210],"rounds,":[154],"based":[155],"history":[158],"partially":[161],"observed":[162],"then":[166],"regret":[169],"that":[171,212],"scales":[172],"logarithmically":[173],"rounds":[178],"observation.":[180],"test":[182],"our":[183],"simulated":[186],"datasets,":[187],"report":[189],"data":[190,199],"Vancouver":[192],"Los":[194],"Angeles,":[195],"earthquake":[197],"from":[200],"Alaska,":[201],"California,":[202],"worldwide.":[204],"Our":[205],"outperforms":[207],"several":[208],"state-of-the-art":[209],"algorithms":[211],"can":[213],"adapted":[215],"non-stationary":[217],"estimation":[220],"across":[221],"datasets":[223],"performance":[225],"metrics.":[226]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
