{"id":"https://openalex.org/W4293370731","doi":"https://doi.org/10.1109/tsp.2022.3198166","title":"Fixed-Horizon Active Hypothesis Testing and Anomaly Detection","display_name":"Fixed-Horizon Active Hypothesis Testing and Anomaly Detection","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4293370731","doi":"https://doi.org/10.1109/tsp.2022.3198166"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2022.3198166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3198166","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-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/A5066966386","display_name":"Dhruva Kartik","orcid":"https://orcid.org/0000-0002-2865-356X"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dhruva Kartik","raw_affiliation_strings":["Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072739163","display_name":"Urbashi Mitra","orcid":"https://orcid.org/0000-0002-8896-1177"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Urbashi Mitra","raw_affiliation_strings":["Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066966386"],"corresponding_institution_ids":["https://openalex.org/I1174212"],"apc_list":null,"apc_paid":null,"fwci":0.2464,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58168043,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"70","issue":null,"first_page":"4170","last_page":"4185"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9961000084877014,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9961000084877014,"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"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9952999949455261,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9948999881744385,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/converse","display_name":"Converse","score":0.7475336790084839},{"id":"https://openalex.org/keywords/asymptotically-optimal-algorithm","display_name":"Asymptotically optimal algorithm","score":0.7017703652381897},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5920098423957825},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5670052170753479},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.5353888273239136},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4498360753059387},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.43872976303100586},{"id":"https://openalex.org/keywords/sequential-analysis","display_name":"Sequential analysis","score":0.4309757947921753},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.41688358783721924},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4110547602176666},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3753088712692261},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1910574734210968},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1737654209136963}],"concepts":[{"id":"https://openalex.org/C2776809875","wikidata":"https://www.wikidata.org/wiki/Q1375963","display_name":"Converse","level":2,"score":0.7475336790084839},{"id":"https://openalex.org/C181789720","wikidata":"https://www.wikidata.org/wiki/Q4812191","display_name":"Asymptotically optimal algorithm","level":2,"score":0.7017703652381897},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5920098423957825},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5670052170753479},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.5353888273239136},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4498360753059387},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.43872976303100586},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.4309757947921753},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.41688358783721924},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4110547602176666},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3753088712692261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1910574734210968},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1737654209136963},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2022.3198166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2022.3198166","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2291874825","display_name":null,"funder_award_id":"N00014-15-1-2550","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"},{"id":"https://openalex.org/G3642481650","display_name":null,"funder_award_id":"2018-04359","funder_id":"https://openalex.org/F4320322581","funder_display_name":"Vetenskapsr\u00e5det"},{"id":"https://openalex.org/G3933102242","display_name":null,"funder_award_id":"CCF-1817200","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"},{"id":"https://openalex.org/G8741606322","display_name":null,"funder_award_id":"503400-78050","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320322581","display_name":"Vetenskapsr\u00e5det","ror":"https://ror.org/03zttf063"},{"id":"https://openalex.org/F4320335353","display_name":"National Science Foundation of Sri Lanka","ror":"https://ror.org/010xaa060"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1584270973","https://openalex.org/W1990503235","https://openalex.org/W2020191275","https://openalex.org/W2034030257","https://openalex.org/W2045030310","https://openalex.org/W2099111195","https://openalex.org/W2101627389","https://openalex.org/W2106864314","https://openalex.org/W2124036594","https://openalex.org/W2138280472","https://openalex.org/W2173497155","https://openalex.org/W2276299490","https://openalex.org/W2545526665","https://openalex.org/W2565335599","https://openalex.org/W2743681928","https://openalex.org/W2887050222","https://openalex.org/W2911128532","https://openalex.org/W2945910721","https://openalex.org/W2951412892","https://openalex.org/W2963506507","https://openalex.org/W2964046421","https://openalex.org/W2964198003","https://openalex.org/W2974976221","https://openalex.org/W2975062169","https://openalex.org/W3080155344","https://openalex.org/W3092008631","https://openalex.org/W3099581246","https://openalex.org/W3110383331","https://openalex.org/W3152994159","https://openalex.org/W3173953251","https://openalex.org/W3179548091","https://openalex.org/W4232896127","https://openalex.org/W4302377426"],"related_works":["https://openalex.org/W2082167858","https://openalex.org/W3091169529","https://openalex.org/W2951679247","https://openalex.org/W2968733425","https://openalex.org/W2102652334","https://openalex.org/W2164663344","https://openalex.org/W2680818516","https://openalex.org/W3163744376","https://openalex.org/W2001786529","https://openalex.org/W2077554657"],"abstract_inverted_index":{"Determining":[0],"the":[1,62,75,80,90,113,137,149],"presence":[2],"of":[3,28,77,85,92,156,162],"an":[4,66,107],"anomaly":[5,67],"or":[6,12,36,45,68],"whether":[7,30,61],"a":[8,15,31,49,153,159],"system":[9,33,81],"is":[10,14,26,72,99,104],"safe":[11,98],"not":[13],"problem":[16,25,103,111],"with":[17],"wide":[18],"applicability.":[19],"The":[20,52,70,123],"model":[21],"adopted":[22],"for":[23],"this":[24],"that":[27,89],"verifying":[29],"multi-component":[32],"has":[34],"anomalies":[35,86],"not.":[37,69],"Components":[38],"can":[39],"be":[40,83,97],"probed":[41],"over":[42],"time":[43],"individually":[44],"as":[46,106],"groups":[47],"in":[48,112,136,148],"data-driven":[50],"manner.":[51],"collected":[53],"observations":[54],"are":[55,121,126,144,170,173],"noisy":[56],"and":[57,119,129,146,167],"contain":[58],"information":[59],"on":[60],"selected":[63],"group":[64],"contains":[65],"aim":[71],"to":[73,82,96],"minimize":[74],"probability":[76,91],"incorrectly":[78],"declaring":[79,94],"free":[84],"while":[87],"ensuring":[88],"correctly":[93],"it":[95],"sufficiently":[100],"large.":[101],"This":[102],"modeled":[105],"active":[108],"hypothesis":[109],"testing":[110],"Neyman-Pearson":[114],"setting.":[115],"Asymptotically":[116],"optimal":[117,134],"rates":[118],"strategies":[120,125],"characterized.":[122],"general":[124],"data":[127],"driven":[128],"outperform":[130],"previously":[131,176],"proposed":[132,177],"asymptotically":[133],"methods":[135],"finite":[138],"sample":[139],"regime.":[140,151],"Furthermore,":[141],"novel":[142],"component-selection":[143],"designed":[145],"analyzed":[147],"non-asymptotic":[150,165],"For":[152],"specific":[154],"class":[155],"problems":[157],"admitting":[158],"key":[160],"form":[161],"symmetry,":[163],"strong":[164],"converse":[166],"achievability":[168],"bounds":[169],"provided":[171],"which":[172],"tighter":[174],"than":[175],"bounds.":[178]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}