{"id":"https://openalex.org/W4327663200","doi":"https://doi.org/10.1109/access.2023.3247956","title":"Learning Adaptive Probabilistic Models for Uncertainty-Aware Air Pollution Prediction","display_name":"Learning Adaptive Probabilistic Models for Uncertainty-Aware Air Pollution Prediction","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4327663200","doi":"https://doi.org/10.1109/access.2023.3247956"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3247956","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3247956","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10050015.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10050015.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048556510","display_name":"Zhiyuan Wu","orcid":"https://orcid.org/0000-0001-5694-2441"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyuan Wu","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041853363","display_name":"Ning Liu","orcid":"https://orcid.org/0000-0003-0418-4516"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Liu","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0418-4516","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060356182","display_name":"Guodong Li","orcid":"https://orcid.org/0000-0002-3250-3919"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guodong Li","raw_affiliation_strings":["Tsinghua–Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-3250-3919","affiliations":[{"raw_affiliation_string":"Tsinghua–Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446465","display_name":"Xinyu Liu","orcid":"https://orcid.org/0009-0009-1047-3139"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Liu","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115695512","display_name":"Yue Wang","orcid":"https://orcid.org/0000-0002-7496-8888"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Wang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100351850","display_name":"Lin Zhang","orcid":"https://orcid.org/0000-0002-4394-2685"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Zhang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4394-2685","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.7417,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.6565781,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"11","issue":null,"first_page":"24971","last_page":"24985"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9835000038146973,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.7960540056228638},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7361887693405151},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.585898220539093},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4910159111022949},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47245529294013977},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.450364351272583},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.44015738368034363},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4376777410507202},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.42384737730026245},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3290752172470093}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7960540056228638},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7361887693405151},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.585898220539093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4910159111022949},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47245529294013977},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.450364351272583},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.44015738368034363},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4376777410507202},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.42384737730026245},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3290752172470093}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3247956","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3247956","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10050015.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:0c14456c1dfe456c92e3966bbdf1a1e4","is_oa":true,"landing_page_url":"https://doaj.org/article/0c14456c1dfe456c92e3966bbdf1a1e4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 24971-24985 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3247956","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1109/access.2023.3247956","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/10005208/10050015.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7699999809265137}],"awards":[{"id":"https://openalex.org/G2015485856","display_name":null,"funder_award_id":"KQTD20170810150821146","funder_id":"https://openalex.org/F4320336569","funder_display_name":"Shenzhen Science and Technology Innovation Program"}],"funders":[{"id":"https://openalex.org/F4320336569","display_name":"Shenzhen Science and Technology Innovation Program","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4327663200.pdf","grobid_xml":"https://content.openalex.org/works/W4327663200.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1969865391","https://openalex.org/W2024760831","https://openalex.org/W2116341502","https://openalex.org/W2134265359","https://openalex.org/W2528639018","https://openalex.org/W2752782242","https://openalex.org/W2808535700","https://openalex.org/W2809035759","https://openalex.org/W2888345420","https://openalex.org/W2901165057","https://openalex.org/W2952135817","https://openalex.org/W2963070905","https://openalex.org/W2964927812","https://openalex.org/W3007989820","https://openalex.org/W3025092043","https://openalex.org/W3034942609","https://openalex.org/W3035498646","https://openalex.org/W3097237405","https://openalex.org/W3133144456","https://openalex.org/W3136518157","https://openalex.org/W3160111009","https://openalex.org/W3176049064","https://openalex.org/W3177318507","https://openalex.org/W3198909301","https://openalex.org/W4205769558","https://openalex.org/W4213164996","https://openalex.org/W4233762729","https://openalex.org/W4235892716","https://openalex.org/W6678815747","https://openalex.org/W6679436768","https://openalex.org/W6720057410","https://openalex.org/W6735236233","https://openalex.org/W6736057607","https://openalex.org/W6737778391","https://openalex.org/W6741832134","https://openalex.org/W6744580074","https://openalex.org/W6748331328","https://openalex.org/W6750254146","https://openalex.org/W6752040014","https://openalex.org/W6753311412","https://openalex.org/W6757740418","https://openalex.org/W6766092863","https://openalex.org/W6788340828","https://openalex.org/W6789043205","https://openalex.org/W6791208785","https://openalex.org/W6791448888","https://openalex.org/W6797648222","https://openalex.org/W6810360122"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4391584540","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4395044357","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W2087346071","https://openalex.org/W2967848559"],"abstract_inverted_index":{"The":[0],"air":[1,25,42,101,116,230],"pollution":[2,26,43,102,117,231],"problem":[3],"has":[4,34],"been":[5,35],"a":[6,140,152,172,191],"serious":[7],"issue":[8],"for":[9,23,64,86,110,186],"public":[10,87],"health":[11],"and":[12,49,55,76,99,195,218,228,243],"city":[13],"development":[14],"in":[15,37,89],"recent":[16],"years,":[17],"which":[18,105],"rises":[19],"an":[20,125,157,183,203],"urgent":[21],"demand":[22],"accurate":[24],"prediction":[27,32,75,103,118,210,245,250],"models.":[28,119],"Traditional":[29],"time":[30,60],"series":[31],"scheme":[33],"challenged":[36],"such":[38],"case":[39],"because":[40],"the":[41,82,111,136,179,187,196,209,235],"signals":[44,138],"can":[45,95,238],"be":[46],"highly":[47],"dynamic":[48],"uncertain.":[50],"Distinct":[51],"latent":[52,193],"physical":[53],"processes":[54],"complex":[56],"environment":[57],"changes":[58],"over":[59],"make":[61],"it":[62],"hard":[63],"one":[65],"fixed":[66],"model":[67,83,154,181,194,201,237],"to":[68,130,160],"give":[69,162],"consistently":[70],"good":[71],"performance.":[72],"Also":[73],"probabilistic":[74,100,165,242],"uncertainty-aware":[77],"estimation":[78],"are":[79,106],"critical":[80],"when":[81],"is":[84,212],"used":[85],"decisions":[88],"practice.":[90],"However,":[91],"few":[92],"previous":[93],"works":[94],"simultaneously":[96,219,239],"provide":[97,171,240],"adaptive":[98,221,244],"abilities,":[104],"important":[107],"practical":[108],"issues":[109],"real-world":[112,229],"implementation":[113],"of":[114,142],"data-driven":[115],"In":[120],"this":[121],"paper":[122],"we":[123,149],"propose":[124],"adversarial":[126],"meta":[127,153,174],"learning":[128,175],"approach":[129],"address":[131],"these":[132],"concerns.":[133],"After":[134],"reformulating":[135],"target":[137],"as":[139,182],"collection":[141],"independent":[143],"but":[144],"similar":[145],"mini-tasks":[146],"along":[147],"time,":[148],"adversarially":[150],"learn":[151],"that":[155,177,205,234],"includes":[156],"implicit":[158],"generator":[159],"adaptively":[161],"more":[163],"informative":[164],"predictions":[166],"across":[167],"tasks.":[168],"We":[169],"further":[170],"bayesian":[173],"interpretation":[176],"recasts":[178],"proposed":[180,236],"approximate":[184],"minimizer":[185],"Wasserstein":[188],"distance":[189],"between":[190],"generative":[192],"true":[197],"data":[198,216],"distribution.":[199],"Our":[200],"provides":[202],"algorithm":[204],"explicitly":[206],"models":[207],"how":[208],"distribution":[211],"conditioned":[213],"on":[214,225],"underlying":[215],"patterns":[217],"gives":[220],"uncertainty":[222],"estimation.":[223],"Experiments":[224],"both":[226],"synthetic":[227],"datasets":[232],"show":[233],"better":[241],"while":[246],"keeping":[247],"stratifying":[248],"point":[249],"error.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-10-10T00:00:00"}