{"id":"https://openalex.org/W3089200980","doi":"https://doi.org/10.1109/tpami.2023.3287042","title":"Experimental Design for Overparameterized Learning With Application to Single Shot Deep Active Learning","display_name":"Experimental Design for Overparameterized Learning With Application to Single Shot Deep Active Learning","publication_year":2023,"publication_date":"2023-06-16","ids":{"openalex":"https://openalex.org/W3089200980","doi":"https://doi.org/10.1109/tpami.2023.3287042","mag":"3089200980","pmid":"https://pubmed.ncbi.nlm.nih.gov/37327090"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2023.3287042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2023.3287042","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5022094360","display_name":"Neta Shoham","orcid":"https://orcid.org/0000-0002-3186-1810"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Neta Shoham","raw_affiliation_strings":["School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel"],"raw_orcid":"https://orcid.org/0000-0002-3186-1810","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072151198","display_name":"Haim Avron","orcid":"https://orcid.org/0000-0002-1688-9030"},"institutions":[{"id":"https://openalex.org/I16391192","display_name":"Tel Aviv University","ror":"https://ror.org/04mhzgx49","country_code":"IL","type":"education","lineage":["https://openalex.org/I16391192"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Haim Avron","raw_affiliation_strings":["School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel"],"raw_orcid":"https://orcid.org/0000-0002-1688-9030","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel","institution_ids":["https://openalex.org/I16391192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16391192"],"apc_list":null,"apc_paid":null,"fwci":0.3173,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.59356901,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"45","issue":"10","first_page":"11766","last_page":"11777"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9993000030517578,"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":0.9993000030517578,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9962999820709229,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9958000183105469,"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/computer-science","display_name":"Computer science","score":0.7752739787101746},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7471809387207031},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6804497241973877},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5922822952270508},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5491311550140381},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5333343148231506},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46232250332832336},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.4604196846485138},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.42719346284866333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7752739787101746},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7471809387207031},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6804497241973877},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5922822952270508},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5491311550140381},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5333343148231506},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46232250332832336},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.4604196846485138},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.42719346284866333},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2023.3287042","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2023.3287042","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:37327090","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37327090","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W3464401","https://openalex.org/W1601485900","https://openalex.org/W1787402657","https://openalex.org/W1985666665","https://openalex.org/W2014018052","https://openalex.org/W2081629343","https://openalex.org/W2104848109","https://openalex.org/W2109033052","https://openalex.org/W2112796928","https://openalex.org/W2114188922","https://openalex.org/W2136325669","https://openalex.org/W2139174822","https://openalex.org/W2150907712","https://openalex.org/W2156195892","https://openalex.org/W2157186153","https://openalex.org/W2440222624","https://openalex.org/W2504108613","https://openalex.org/W2566079294","https://openalex.org/W2597787948","https://openalex.org/W2767623047","https://openalex.org/W2809090039","https://openalex.org/W2891798045","https://openalex.org/W2922153390","https://openalex.org/W2950958145","https://openalex.org/W2955906643","https://openalex.org/W2962819230","https://openalex.org/W2963491601","https://openalex.org/W2963518130","https://openalex.org/W2963902936","https://openalex.org/W2964034630","https://openalex.org/W2970217468","https://openalex.org/W2970291982","https://openalex.org/W2970728427","https://openalex.org/W2971043187","https://openalex.org/W2971363918","https://openalex.org/W2994747787","https://openalex.org/W2994872659","https://openalex.org/W2995188922","https://openalex.org/W2996360471","https://openalex.org/W2996603747","https://openalex.org/W3009460743","https://openalex.org/W3018252856","https://openalex.org/W3020919495","https://openalex.org/W3084611071","https://openalex.org/W3111350549","https://openalex.org/W3137695714","https://openalex.org/W3214372234","https://openalex.org/W4206410067","https://openalex.org/W4233536583","https://openalex.org/W4300031234","https://openalex.org/W6680100795","https://openalex.org/W6680569831","https://openalex.org/W6681302627","https://openalex.org/W6735374517","https://openalex.org/W6743579140","https://openalex.org/W6745907806","https://openalex.org/W6747231328","https://openalex.org/W6747679287","https://openalex.org/W6752495264","https://openalex.org/W6761496057","https://openalex.org/W6763607942","https://openalex.org/W6765648209","https://openalex.org/W6766403704","https://openalex.org/W6766936207","https://openalex.org/W6768106036","https://openalex.org/W6771396984","https://openalex.org/W6771673757","https://openalex.org/W6774542976","https://openalex.org/W6796439309","https://openalex.org/W6948022635"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1001352512","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138"],"abstract_inverted_index":{"The":[0],"impressive":[1],"performance":[2,129,149],"exhibited":[3],"by":[4,46,199],"modern":[5,94,123],"machine":[6,95],"learning":[7,71,96,124,198],"models":[8,16,97,132,152],"hinges":[9],"on":[10,17,76,81,143],"the":[11,49,70,127,147,188,194],"ability":[12],"to":[13,28,42,63,67,86,109,134],"train":[14],"such":[15,98],"a":[18,56,173,201],"very":[19],"large":[20,29],"amounts":[21],"of":[22,31,130,150,165,190,196],"labeled":[23,32,65],"data.":[24],"However,":[25],"since":[26],"access":[27],"volumes":[30],"data":[33,61],"is":[34,40,55,157,177],"often":[35],"limited":[36],"or":[37,164],"expensive,":[38],"it":[39],"desirable":[41],"alleviate":[43],"this":[44,160,169],"bottleneck":[45],"carefully":[47],"curating":[48],"training":[50],"set.":[51],"Optimal":[52],"experimental":[53,78,115,140],"design":[54,79,116,141,174],"well-established":[57],"paradigm":[58],"for":[59,180,204],"selecting":[60,82],"point":[62],"be":[64,110,135],"so":[66,138],"maximally":[68],"inform":[69],"process.":[72],"Unfortunately,":[73],"classical":[74,114,139],"theory":[75],"optimal":[77],"focuses":[80,142],"examples":[83],"in":[84,121,159,193],"order":[85],"learn":[87],"underparameterized":[88,131],"(and":[89],"thus,":[90],"non-interpolative)":[91],"models,":[92],"while":[93,146],"as":[99,156],"deep":[100,197,207],"neural":[101],"networks":[102],"are":[103,107,118],"overparameterized,":[104],"and":[105,183,185],"oftentimes":[106],"trained":[108],"interpolative.":[111],"As":[112],"such,":[113],"methods":[117],"not":[119],"applicable":[120],"many":[122],"setups.":[125],"Indeed,":[126],"predictive":[128,148],"tends":[133],"variance":[136,144],"dominated,":[137],"reduction,":[145],"overparameterized":[151,181],"can":[153],"also":[154],"be,":[155],"shown":[158],"paper,":[161],"bias":[162],"dominated":[163],"mixed":[166],"nature.":[167],"In":[168],"paper":[170],"we":[171,186],"propose":[172],"strategy":[175],"that":[176],"well":[178],"suited":[179],"regression":[182],"interpolation,":[184],"demonstrate":[187],"applicability":[189],"our":[191],"method":[192],"context":[195],"proposing":[200],"new":[202],"algorithm":[203],"single":[205],"shot":[206],"active":[208],"learning.":[209]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}