{"id":"https://openalex.org/W2585411378","doi":"https://doi.org/10.1109/isit.2017.8006580","title":"Statistical and computational phase transitions in spiked tensor estimation","display_name":"Statistical and computational phase transitions in spiked tensor estimation","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2585411378","doi":"https://doi.org/10.1109/isit.2017.8006580","mag":"2585411378"},"language":"en","primary_location":{"id":"doi:10.1109/isit.2017.8006580","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2017.8006580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1701.08010","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Thibault Lesieur","orcid":null},"institutions":[{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Thibault Lesieur","raw_affiliation_strings":["IPhT, Univ. Paris-Saclay"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IPhT, Univ. Paris-Saclay","institution_ids":["https://openalex.org/I277688954"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Leo Miolane","orcid":null},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I29607241","display_name":"\u00c9cole Normale Sup\u00e9rieure - PSL","ror":"https://ror.org/05a0dhs15","country_code":"FR","type":"other","lineage":["https://openalex.org/I2746051580","https://openalex.org/I29607241"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Leo Miolane","raw_affiliation_strings":["INRIA, ENS, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"INRIA, ENS, Paris, France","institution_ids":["https://openalex.org/I1326498283","https://openalex.org/I29607241"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Marc Lelarge","orcid":null},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]},{"id":"https://openalex.org/I29607241","display_name":"\u00c9cole Normale Sup\u00e9rieure - PSL","ror":"https://ror.org/05a0dhs15","country_code":"FR","type":"other","lineage":["https://openalex.org/I2746051580","https://openalex.org/I29607241"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Marc Lelarge","raw_affiliation_strings":["INRIA, ENS, Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"INRIA, ENS, Paris, France","institution_ids":["https://openalex.org/I1326498283","https://openalex.org/I29607241"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Florent Krzakala","orcid":null},"institutions":[{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210141120","display_name":"Sorbonne University Abu Dhabi","ror":"https://ror.org/03e1ymy32","country_code":"AE","type":"education","lineage":["https://openalex.org/I4210141120"]}],"countries":["AE","FR"],"is_corresponding":false,"raw_author_name":"Florent Krzakala","raw_affiliation_strings":["PSL, UPMC & Sorbonne Univ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"PSL, UPMC & Sorbonne Univ","institution_ids":["https://openalex.org/I4210141120","https://openalex.org/I39804081"]}]},{"author_position":"last","author":{"id":null,"display_name":"Lenka Zdeborova","orcid":null},"institutions":[{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Lenka Zdeborova","raw_affiliation_strings":["IPhT, Univ. Paris-Saclay"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IPhT, Univ. Paris-Saclay","institution_ids":["https://openalex.org/I277688954"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2544,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.87250384,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"511","last_page":"515"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9426000118255615,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6187999844551086},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.6054999828338623},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6003999710083008},{"id":"https://openalex.org/keywords/conjecture","display_name":"Conjecture","score":0.5371000170707703},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5157999992370605},{"id":"https://openalex.org/keywords/polynomial","display_name":"Polynomial","score":0.5135999917984009},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.4309999942779541},{"id":"https://openalex.org/keywords/square","display_name":"Square (algebra)","score":0.4228000044822693}],"concepts":[{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6187999844551086},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.6054999828338623},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6003999710083008},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5741999745368958},{"id":"https://openalex.org/C2780990831","wikidata":"https://www.wikidata.org/wiki/Q319141","display_name":"Conjecture","level":2,"score":0.5371000170707703},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49900001287460327},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.4309999942779541},{"id":"https://openalex.org/C135692309","wikidata":"https://www.wikidata.org/wiki/Q111124","display_name":"Square (algebra)","level":2,"score":0.4228000044822693},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.39730000495910645},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3790000081062317},{"id":"https://openalex.org/C149288129","wikidata":"https://www.wikidata.org/wiki/Q185357","display_name":"Phase transition","level":2,"score":0.34529998898506165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.33709999918937683},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.329800009727478},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.299699991941452},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C90652560","wikidata":"https://www.wikidata.org/wiki/Q11091747","display_name":"Minimum mean square error","level":3,"score":0.28290000557899475},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.2824000120162964},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.2766999900341034},{"id":"https://openalex.org/C81793267","wikidata":"https://www.wikidata.org/wiki/Q7180962","display_name":"Phase retrieval","level":3,"score":0.2741999924182892},{"id":"https://openalex.org/C162392398","wikidata":"https://www.wikidata.org/wiki/Q272404","display_name":"Finite set","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.25270000100135803}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/isit.2017.8006580","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit.2017.8006580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1701.08010","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1701.08010","pdf_url":"https://arxiv.org/pdf/1701.08010","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:cea-01555504v1","is_oa":true,"landing_page_url":"https://cea.hal.science/cea-01555504","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISIT 2017 - IEEE International Symposium on Information Theory, Jun 2017, Aachen, Germany. pp.511 - 515, &#x27E8;10.1109/ISIT.2017.8006580&#x27E9;","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1701.08010","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1701.08010","pdf_url":"https://arxiv.org/pdf/1701.08010","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1572652170","https://openalex.org/W1677159791","https://openalex.org/W1700527341","https://openalex.org/W1922029667","https://openalex.org/W1990755770","https://openalex.org/W2040969041","https://openalex.org/W2048964254","https://openalex.org/W2072438896","https://openalex.org/W2086177078","https://openalex.org/W2119412403","https://openalex.org/W2151781750","https://openalex.org/W2161410247","https://openalex.org/W2332917886","https://openalex.org/W2571114381","https://openalex.org/W2610971674","https://openalex.org/W2964282092","https://openalex.org/W6667157200","https://openalex.org/W6674373464","https://openalex.org/W6674881146","https://openalex.org/W6676039487","https://openalex.org/W6718058154","https://openalex.org/W6719972455","https://openalex.org/W6731185149","https://openalex.org/W6765981223"],"related_works":[],"abstract_inverted_index":{"We":[0,12],"consider":[1],"tensor":[2],"factorizations":[3],"using":[4],"a":[5,9,48,60,70],"generative":[6],"model":[7],"and":[8,24,40,53,80],"Bayesian":[10],"approach.":[11],"compute":[13],"rigorously":[14],"the":[15,18,33,45,78],"mutual":[16],"information,":[17],"Minimal":[19],"Mean":[20],"Square":[21],"Error":[22],"(MMSE),":[23],"unveil":[25],"information-theoretic":[26],"phase":[27],"transitions.":[28],"In":[29],"addition,":[30],"we":[31,81],"study":[32],"performance":[34],"of":[35,51],"Approximate":[36],"Message":[37],"Passing":[38],"(AMP)":[39],"show":[41],"that":[42,54,83],"it":[43],"achieves":[44],"MMSE":[46,79],"for":[47],"large":[49],"set":[50],"parameters,":[52],"factorization":[55],"is":[56],"algorithmically":[57],"\u201ceasy\u201d":[58],"in":[59],"much":[61],"wider":[62],"region":[63,72],"than":[64],"previously":[65],"believed.":[66],"It":[67],"exists,":[68],"however,":[69],"\u201chard\u201d":[71],"where":[73],"AMP":[74],"fails":[75],"to":[76],"reach":[77],"conjecture":[82],"no":[84],"polynomial":[85],"algorithm":[86],"will":[87],"improve":[88],"on":[89],"AMP.":[90]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2017-02-10T00:00:00"}
