{"id":"https://openalex.org/W2411442487","doi":"https://doi.org/10.1109/icra.2016.7487143","title":"Drifting Gaussian processes with varying neighborhood sizes for online model learning","display_name":"Drifting Gaussian processes with varying neighborhood sizes for online model learning","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2411442487","doi":"https://doi.org/10.1109/icra.2016.7487143","mag":"2411442487"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2016.7487143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5071976030","display_name":"Franziska Meier","orcid":"https://orcid.org/0000-0003-1640-4619"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I4210135521","display_name":"Max Planck Institute for Intelligent Systems","ror":"https://ror.org/04fq9j139","country_code":"DE","type":"facility","lineage":["https://openalex.org/I149899117","https://openalex.org/I4210135521"]}],"countries":["DE","US"],"is_corresponding":false,"raw_author_name":"Franziska Meier","raw_affiliation_strings":["Computational Learning and Motor Control Lab, University of Southern California, Los Angeles, CA, USA","Max-Planck-Institute for Intelligent Systems, T\u00fcbingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computational Learning and Motor Control Lab, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Max-Planck-Institute for Intelligent Systems, T\u00fcbingen, Germany","institution_ids":["https://openalex.org/I4210135521"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029642293","display_name":"Stefan Schaal","orcid":"https://orcid.org/0000-0001-5660-1874"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stefan Schaal","raw_affiliation_strings":["University of Southern California, Los Angeles, CA, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Southern California, Los Angeles, CA, US","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"264","last_page":"269"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9998999834060669,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9998999834060669,"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/T11236","display_name":"Control Systems and Identification","score":0.9746000170707703,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9265999794006348,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.7692675590515137},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.7373414039611816},{"id":"https://openalex.org/keywords/online-learning","display_name":"Online learning","score":0.5412272214889526},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5347105264663696},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5280160307884216},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.502875030040741},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4935961961746216},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4684830904006958},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.4466778635978699},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.417934387922287}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7692675590515137},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.7373414039611816},{"id":"https://openalex.org/C2986087404","wikidata":"https://www.wikidata.org/wiki/Q15946010","display_name":"Online learning","level":2,"score":0.5412272214889526},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5347105264663696},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5280160307884216},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.502875030040741},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4935961961746216},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4684830904006958},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.4466778635978699},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.417934387922287},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","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},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra.2016.7487143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.mpg.de:item_2394973","is_oa":false,"landing_page_url":"http://hdl.handle.net/11858/00-001M-0000-002D-1A38-B","pdf_url":null,"source":{"id":"https://openalex.org/S4306400654","display_name":"MPG.PuRe (Max Planck Society)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149899117","host_organization_name":"Max Planck Society","host_organization_lineage":["https://openalex.org/I149899117"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA 2016)","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W137285897","https://openalex.org/W1497915382","https://openalex.org/W1571870753","https://openalex.org/W1597173708","https://openalex.org/W1746819321","https://openalex.org/W1777124189","https://openalex.org/W2014903366","https://openalex.org/W2069017697","https://openalex.org/W2079315820","https://openalex.org/W2091651498","https://openalex.org/W2099768828","https://openalex.org/W2122377431","https://openalex.org/W2129564505","https://openalex.org/W2144902422","https://openalex.org/W2150582689","https://openalex.org/W2167804690","https://openalex.org/W2170912685","https://openalex.org/W2172114485","https://openalex.org/W2323447981","https://openalex.org/W2952795334","https://openalex.org/W4205362845","https://openalex.org/W4211049957","https://openalex.org/W4241649586","https://openalex.org/W6605566567","https://openalex.org/W6630053902","https://openalex.org/W6637968757","https://openalex.org/W6674989108","https://openalex.org/W6678278778","https://openalex.org/W6681302627","https://openalex.org/W6682278119"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"Computationally":[0],"efficient":[1,33],"online":[2,61],"learning":[3,23,37,41,58,62,89,180],"of":[4,31,63,96,118,122,136],"non-stationary":[5],"models":[6],"remains":[7],"a":[8,50,79,116,119],"difficult":[9],"challenge.":[10],"A":[11,111],"robust":[12,40,54],"and":[13,34,55,127,174],"reliable":[14],"algorithm":[15],"could":[16],"have":[17],"great":[18],"impact":[19],"on":[20,27,66,141,170,177],"problems":[21],"in":[22,154],"control.":[24],"Recent":[25],"work":[26],"combining":[28],"the":[29,94,103,138,143],"worlds":[30],"computationally":[32],"locally":[35],"adaptive":[36],"algorithms":[38],"with":[39],"frameworks":[42],"such":[43,74],"as":[44,75],"Gaussian":[45,98,113],"process":[46,114],"regression":[47],"has":[48],"taken":[49],"step":[51],"towards":[52],"both":[53],"real-time":[56],"capable":[57],"systems.":[59,90],"However,":[60],"model":[64],"parameters":[65],"streaming":[67],"data":[68,76,106,125,173],"-":[69,81,153,156],"that":[70,105],"is":[71,107,145],"strongly":[72],"correlated,":[73],"arriving":[77],"along":[78,109],"trajectory":[80],"can":[82],"still":[83],"create":[84],"serious":[85],"issues":[86],"for":[87,163],"many":[88],"Here":[91],"we":[92,148],"investigate":[93],"idea":[95],"drifting":[97,112,157],"processes":[99],"which":[100,142],"explicitly":[101],"exploit":[102],"fact":[104],"generated":[108,172],"trajectories.":[110],"keeps":[115],"history":[117],"constant":[120],"number":[121],"recently":[123],"observed":[124],"points":[126],"updates":[128],"its":[129],"hyper-parameters":[130],"at":[131],"each":[132],"time":[133],"step.":[134],"Instead":[135],"optimizing":[137],"neighborhood":[139],"size":[140],"GP":[144],"trained":[146],"on,":[147],"propose":[149],"to":[150],"use":[151],"several":[152],"parallel":[155],"GPs":[158],"whose":[159],"predictions":[160],"are":[161],"combined":[162],"query":[164],"points.":[165],"We":[166],"illustrate":[167],"our":[168],"approach":[169],"synthetically":[171],"successfully":[175],"evaluate":[176],"inverse":[178],"dynamics":[179],"tasks.":[181]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}