{"id":"https://openalex.org/W2202955057","doi":"https://doi.org/10.1162/neco_a_00844","title":"Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation","display_name":"Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation","publication_year":2016,"publication_date":"2016-05-12","ids":{"openalex":"https://openalex.org/W2202955057","doi":"https://doi.org/10.1162/neco_a_00844","mag":"2202955057","pmid":"https://pubmed.ncbi.nlm.nih.gov/27171983"},"language":"en","primary_location":{"id":"doi:10.1162/neco_a_00844","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_00844","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1508.00085","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088128639","display_name":"Ikko Yamane","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ikko Yamane","raw_affiliation_strings":["Graduate School of Frontier Sciences, University of Tokyo, Kashiwa-shi, Chiba 277-8561, Japan","Univ.\\ of Tokyo"],"affiliations":[{"raw_affiliation_string":"Graduate School of Frontier Sciences, University of Tokyo, Kashiwa-shi, Chiba 277-8561, Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Univ.\\ of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047718379","display_name":"Hiroaki Sasaki","orcid":"https://orcid.org/0000-0001-9897-8557"},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroaki Sasaki","raw_affiliation_strings":["Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma-shi, Nara 630-0192, Japan","Nara Institute of Science & Technology#TAB#"],"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma-shi, Nara 630-0192, Japan","institution_ids":["https://openalex.org/I75917431"]},{"raw_affiliation_string":"Nara Institute of Science & Technology#TAB#","institution_ids":["https://openalex.org/I75917431"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072744508","display_name":"Masashi Sugiyama","orcid":"https://orcid.org/0000-0001-6658-6743"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masashi Sugiyama","raw_affiliation_strings":["Graduate School of Frontier Sciences, University of Tokyo, Kashiwa-shi, Chiba 277-8561, Japan","Univ.\\ of Tokyo"],"affiliations":[{"raw_affiliation_string":"Graduate School of Frontier Sciences, University of Tokyo, Kashiwa-shi, Chiba 277-8561, Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Univ.\\ of Tokyo","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088128639"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.4434,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.77643619,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"28","issue":"7","first_page":"1388","last_page":"1410"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9951000213623047,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9951000213623047,"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":0.994700014591217,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9943000078201294,"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/density-estimation","display_name":"Density estimation","score":0.777571439743042},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.6638051867485046},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6345251798629761},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5580534934997559},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5388742089271545},{"id":"https://openalex.org/keywords/density-gradient","display_name":"Density gradient","score":0.5028929114341736},{"id":"https://openalex.org/keywords/gradient-method","display_name":"Gradient method","score":0.43493446707725525},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4305817484855652},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.42684996128082275},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4241909384727478},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4158388674259186},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34273070096969604},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.26656848192214966},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.10637354850769043},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.0959305465221405}],"concepts":[{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.777571439743042},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6638051867485046},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6345251798629761},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5580534934997559},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5388742089271545},{"id":"https://openalex.org/C2778892434","wikidata":"https://www.wikidata.org/wiki/Q1871401","display_name":"Density gradient","level":2,"score":0.5028929114341736},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.43493446707725525},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4305817484855652},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.42684996128082275},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4241909384727478},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4158388674259186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34273070096969604},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26656848192214966},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.10637354850769043},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0959305465221405},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1162/neco_a_00844","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_00844","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},{"id":"pmid:27171983","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/27171983","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":"Neural computation","raw_type":null},{"id":"pmh:oai:arXiv.org:1508.00085","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1508.00085","pdf_url":"https://arxiv.org/pdf/1508.00085","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":"doi:10.48550/arxiv.1508.00085","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1508.00085","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1508.00085","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1508.00085","pdf_url":"https://arxiv.org/pdf/1508.00085","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":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2202955057.pdf","grobid_xml":"https://content.openalex.org/works/W2202955057.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1540007258","https://openalex.org/W1855110042","https://openalex.org/W1971901677","https://openalex.org/W1977005232","https://openalex.org/W2016013563","https://openalex.org/W2017966952","https://openalex.org/W2022686119","https://openalex.org/W2064769840","https://openalex.org/W2067191022","https://openalex.org/W2115003579","https://openalex.org/W2123737232","https://openalex.org/W2126288184","https://openalex.org/W2130903752","https://openalex.org/W2133491790","https://openalex.org/W2137420707","https://openalex.org/W2143104527","https://openalex.org/W2144393430","https://openalex.org/W2144752499","https://openalex.org/W2149376583","https://openalex.org/W2159128898","https://openalex.org/W2162888803","https://openalex.org/W2164500538","https://openalex.org/W2170131942","https://openalex.org/W2395090900","https://openalex.org/W2913340405","https://openalex.org/W2949548704","https://openalex.org/W3104240813","https://openalex.org/W4235169531","https://openalex.org/W4238957295"],"related_works":["https://openalex.org/W1855110042","https://openalex.org/W2369263553","https://openalex.org/W2612152831","https://openalex.org/W2373152179","https://openalex.org/W2156273800","https://openalex.org/W2127599432","https://openalex.org/W2119950020","https://openalex.org/W2154430791","https://openalex.org/W2363014025","https://openalex.org/W2202955057"],"abstract_inverted_index":{"Log-density":[0],"gradient":[1,33,49,63,106,123,137],"estimation":[2,66,107,138],"is":[3,34,85,99],"a":[4,56,112],"fundamental":[5],"statistical":[6],"problem":[7,103],"and":[8,16,28,70,114,139],"possesses":[9],"various":[10],"practical":[11],"applications":[12],"such":[13],"as":[14,111],"clustering":[15],"measuring":[17],"nongaussianity.":[18],"A":[19],"naive":[20],"two-step":[21,78],"approach":[22],"of":[23,82,90,104,130],"first":[24],"estimating":[25],"the":[26,61,77,88,102,120,128,131],"density":[27,39,65],"then":[29],"taking":[30],"its":[31],"log":[32],"unreliable":[35],"because":[36],"an":[37,46],"accurate":[38,47],"estimate":[40,60],"does":[41],"not":[42],"necessarily":[43],"lead":[44],"to":[45,58,72,86,100,119],"log-density":[48,62,105,122,136],"estimate.":[50],"To":[51],"cope":[52],"with":[53],"this":[54,83,91],"problem,":[55],"method":[57,93,134],"directly":[59],"without":[64],"has":[67],"been":[68],"explored":[69],"demonstrated":[71],"work":[73],"much":[74],"better":[75],"than":[76],"method.":[79],"The":[80],"objective":[81],"letter":[84],"improve":[87],"performance":[89],"direct":[92,121],"in":[94,108,135],"multidimensional":[95],"cases.":[96],"Our":[97],"idea":[98],"regard":[101],"each":[109],"dimension":[110],"task":[113],"apply":[115],"regularized":[116],"multitask":[117,133],"learning":[118],"estimator.":[124],"We":[125],"experimentally":[126],"demonstrate":[127],"usefulness":[129],"proposed":[132],"mode-seeking":[140],"clustering.":[141]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}