{"id":"https://openalex.org/W7164054531","doi":"https://doi.org/10.1145/3798129.3800791","title":"High-Accuracy List-Decodable Mean Estimation","display_name":"High-Accuracy List-Decodable Mean Estimation","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164054531","doi":"https://doi.org/10.1145/3798129.3800791"},"language":null,"primary_location":{"id":"doi:10.1145/3798129.3800791","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3798129.3800791","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 58th Annual ACM Symposium on Theory of Computing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3798129.3800791","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043358342","display_name":"Ziyun Chen","orcid":"https://orcid.org/0009-0001-7214-6856"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ziyun Chen","raw_affiliation_strings":["University of Washington, Seattle, USA"],"raw_orcid":"https://orcid.org/0009-0001-7214-6856","affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, USA","institution_ids":["https://openalex.org/I201448701","https://openalex.org/I58610484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049956691","display_name":"Spencer Compton","orcid":"https://orcid.org/0009-0009-4161-4651"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Spencer Compton","raw_affiliation_strings":["Stanford University, Stanford, USA"],"raw_orcid":"https://orcid.org/0009-0009-4161-4651","affiliations":[{"raw_affiliation_string":"Stanford University, Stanford, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024664750","display_name":"Daniel M. Kane","orcid":"https://orcid.org/0009-0007-9647-2609"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel M. Kane","raw_affiliation_strings":["University of California at San Diego, La Jolla, USA"],"raw_orcid":"https://orcid.org/0009-0007-9647-2609","affiliations":[{"raw_affiliation_string":"University of California at San Diego, La Jolla, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045207457","display_name":"J. Li","orcid":"https://orcid.org/0009-0003-5035-3728"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jerry Li","raw_affiliation_strings":["University of Washington, Seattle, USA"],"raw_orcid":"https://orcid.org/0009-0003-5035-3728","affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, USA","institution_ids":["https://openalex.org/I201448701","https://openalex.org/I58610484"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65823492,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"767","last_page":"776"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.07429999858140945,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.07429999858140945,"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/T10136","display_name":"Statistical Methods and Inference","score":0.0544000007212162,"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/T11719","display_name":"Data Quality and Management","score":0.05260000005364418,"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/set","display_name":"Set (abstract data type)","score":0.5378000140190125},{"id":"https://openalex.org/keywords/element","display_name":"Element (criminal law)","score":0.5260999798774719},{"id":"https://openalex.org/keywords/ask-price","display_name":"Ask price","score":0.5123999714851379},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4205999970436096},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.40139999985694885},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.40049999952316284}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5565000176429749},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5335000157356262},{"id":"https://openalex.org/C200288055","wikidata":"https://www.wikidata.org/wiki/Q2621792","display_name":"Element (criminal law)","level":2,"score":0.5260999798774719},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.5123999714851379},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4205999970436096},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.40130001306533813},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.40049999952316284},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.373199999332428},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33309999108314514},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2741999924182892},{"id":"https://openalex.org/C30872290","wikidata":"https://www.wikidata.org/wiki/Q1172389","display_name":"Data element","level":3,"score":0.260699987411499},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3798129.3800791","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3798129.3800791","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 58th Annual ACM Symposium on Theory of Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3798129.3800791","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3798129.3800791","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 58th Annual ACM Symposium on Theory of Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1479841653","display_name":null,"funder_award_id":"214324","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4109185202","display_name":null,"funder_award_id":"928589","funder_id":"https://openalex.org/F4320306164","funder_display_name":"Simons Foundation"},{"id":"https://openalex.org/G6664463520","display_name":null,"funder_award_id":"CCF-2107547","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6786676679","display_name":null,"funder_award_id":"AF-2341890","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7269554749","display_name":null,"funder_award_id":"245058","funder_id":"https://openalex.org/F4320306164","funder_display_name":"Simons Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306164","display_name":"Simons Foundation","ror":"https://ror.org/01cmst727"},{"id":"https://openalex.org/F4320333566","display_name":"National Defense Science and Engineering Graduate","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1967228187","https://openalex.org/W1995851745","https://openalex.org/W2036447017","https://openalex.org/W2138092283","https://openalex.org/W2148858749","https://openalex.org/W2171394996","https://openalex.org/W2554864439","https://openalex.org/W2808754399","https://openalex.org/W2942689850","https://openalex.org/W2963351358","https://openalex.org/W2964288084","https://openalex.org/W3002207671","https://openalex.org/W3093456580","https://openalex.org/W3172094718","https://openalex.org/W4249736682","https://openalex.org/W4281667950","https://openalex.org/W4376639487","https://openalex.org/W4376639555","https://openalex.org/W4376639593","https://openalex.org/W4386089604","https://openalex.org/W4415799005","https://openalex.org/W7133214675","https://openalex.org/W7133247928"],"related_works":[],"abstract_inverted_index":{"In":[0,99],"list-decodable":[1,119,162],"learning,":[2],"we":[3,102,128],"are":[4],"given":[5,123],"a":[6,20,36,61,130],"set":[7],"of":[8,15,39,48,64,80,136,145],"data":[9],"points":[10,17],"such":[11,42],"that":[12,43,142],"an":[13],"\u03b1-fraction":[14],"these":[16],"come":[18],"from":[19],"\u201cnice\u201d":[21],"distribution":[22],"D,":[23],"for":[24],"some":[25,52,91],"small":[26],"\u03b1":[27,137],"\u226a":[28],"1,":[29],"and":[30,138],"the":[31,154],"goal":[32],"is":[33,60,106,108],"to":[34,111],"output":[35,129],"short":[37],"list":[38,50,76,114,133,147],"candidate":[40],"solutions,":[41],"at":[44,150],"least":[45],"one":[46,143],"element":[47,144],"this":[49,67,100,105,146,159],"recovers":[51],"non-trivial":[53],"information":[54],"about":[55],"D.":[56],"By":[57],"now,":[58],"there":[59],"large":[62],"body":[63],"work":[65],"on":[66],"topic;":[68],"however,":[69],"while":[70],"many":[71],"algorithms":[72,84],"can":[73,127],"achieve":[74],"optimal":[75],"size":[77,115],"in":[78,90,118,134],"terms":[79,135],"\u03b1,":[81],"all":[82],"known":[83],"must":[85],"incur":[86],"error":[87,149],"which":[88],"decays,":[89],"cases":[92],"quite":[93],"poorly,":[94],"with":[95,116,153],"1":[96],"/":[97],"\u03b1.":[98],"paper,":[101],"ask":[103],"if":[104],"inherent:":[107],"it":[109],"possible":[110],"trade":[112],"off":[113],"accuracy":[117],"learning?":[120],"More":[121],"formally,":[122],"\u03b5":[124,152],">":[125],"0,":[126],"slightly":[131],"larger":[132],"\u03b5,":[139],"but":[140],"so":[141],"has":[148],"most":[151],"ground":[155],"truth?":[156],"We":[157],"call":[158],"problem":[160],"high-accuracy":[161],"learning.":[163]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-06-10T00:00:00"}