{"id":"https://openalex.org/W4390188781","doi":"https://doi.org/10.1109/hpec58863.2023.10363486","title":"Decreasing the Computing Time of Bayesian Optimization Using Generalizable Memory Pruning","display_name":"Decreasing the Computing Time of Bayesian Optimization Using Generalizable Memory Pruning","publication_year":2023,"publication_date":"2023-09-25","ids":{"openalex":"https://openalex.org/W4390188781","doi":"https://doi.org/10.1109/hpec58863.2023.10363486"},"language":"en","primary_location":{"id":"doi:10.1109/hpec58863.2023.10363486","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/hpec58863.2023.10363486","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"},"type":"article","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/A5020179556","display_name":"Alexander E. Siemenn","orcid":"https://orcid.org/0000-0001-8841-7887"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander E. Siemenn","raw_affiliation_strings":["Massachusetts Institute of Technology,Department of Mechanical Engineering,Cambridge,MA,USA","Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology,Department of Mechanical Engineering,Cambridge,MA,USA","institution_ids":["https://openalex.org/I63966007"]},{"raw_affiliation_string":"Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023456690","display_name":"Tonio Buonassisi","orcid":null},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tonio Buonassisi","raw_affiliation_strings":["Massachusetts Institute of Technology,Department of Mechanical Engineering,Cambridge,MA,USA","Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Massachusetts Institute of Technology,Department of Mechanical Engineering,Cambridge,MA,USA","institution_ids":["https://openalex.org/I63966007"]},{"raw_affiliation_string":"Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I63966007"],"apc_list":null,"apc_paid":null,"fwci":0.5512,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72428942,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"13","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9995999932289124,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9957000017166138,"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/pruning","display_name":"Pruning","score":0.7961808443069458},{"id":"https://openalex.org/keywords/surrogate-model","display_name":"Surrogate model","score":0.763688325881958},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7337455749511719},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5320775508880615},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4946870505809784},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.42565280199050903},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4212302565574646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3515543043613434},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2734726369380951},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.26569271087646484}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7961808443069458},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.763688325881958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7337455749511719},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5320775508880615},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4946870505809784},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.42565280199050903},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4212302565574646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3515543043613434},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2734726369380951},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.26569271087646484},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpec58863.2023.10363486","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/hpec58863.2023.10363486","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE High Performance Extreme Computing Conference (HPEC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W137285897","https://openalex.org/W1507599800","https://openalex.org/W1510052597","https://openalex.org/W1599284199","https://openalex.org/W1693986406","https://openalex.org/W1964746686","https://openalex.org/W1992536060","https://openalex.org/W1992985800","https://openalex.org/W1994005439","https://openalex.org/W2045518038","https://openalex.org/W2061144551","https://openalex.org/W2099201756","https://openalex.org/W2099768828","https://openalex.org/W2108114251","https://openalex.org/W2131627640","https://openalex.org/W2192203593","https://openalex.org/W2883672905","https://openalex.org/W2888436131","https://openalex.org/W2962824535","https://openalex.org/W2977748703","https://openalex.org/W2998788451","https://openalex.org/W3042036888","https://openalex.org/W3097943555","https://openalex.org/W3106525532","https://openalex.org/W3155456766","https://openalex.org/W3196282477","https://openalex.org/W3203436218","https://openalex.org/W4205757732","https://openalex.org/W4211049957","https://openalex.org/W4297629922","https://openalex.org/W4378469368","https://openalex.org/W6605566567","https://openalex.org/W6637591494","https://openalex.org/W6676576766","https://openalex.org/W6732231852","https://openalex.org/W6734918142","https://openalex.org/W6764988152"],"related_works":["https://openalex.org/W4380627621","https://openalex.org/W4292081304","https://openalex.org/W2950792054","https://openalex.org/W3104422856","https://openalex.org/W4206864338","https://openalex.org/W4287867179","https://openalex.org/W3134690064","https://openalex.org/W4210726438","https://openalex.org/W4287752080","https://openalex.org/W4237912051"],"abstract_inverted_index":{"Bayesian":[0],"optimization":[1],"(BO)":[2],"suffers":[3],"from":[4,133],"long":[5,16],"computing":[6,17,67,127,183],"times":[7,18,128],"when":[8],"processing":[9],"highly-dimensional":[10],"or":[11,42],"large":[12],"data":[13,44,162],"sets.":[14],"These":[15],"are":[19,176],"a":[20,29,95,123,134,139,144],"result":[21],"of":[22,36,69,79,99,106,131,156],"the":[23,34,66,70,80,154,157,179],"Gaussian":[24],"process":[25],"surrogate":[26,59,82,111,166],"model":[27,112,174],"having":[28],"polynomial":[30],"time":[31,51],"complexity":[32,52],"with":[33,109],"number":[35],"experiments.":[37],"Running":[38],"BO":[39,71,97,132],"on":[40,178],"high-dimensional":[41],"massive":[43],"sets":[45],"becomes":[46],"intractable":[47],"due":[48],"to":[49,64,138],"this":[50,91,117],"scaling,":[53],"in":[54,125],"turn,":[55],"hindering":[56],"experimentation.":[57],"Alternative":[58],"models":[60],"have":[61],"been":[62],"developed":[63],"reduce":[65],"utilization":[68],"procedure,":[72],"however,":[73],"these":[74],"methods":[75],"require":[76],"mathematical":[77],"alteration":[78],"inherit":[81],"function,":[83],"pigeonholing":[84],"use":[85],"into":[86],"only":[87],"that":[88,142],"function.":[89,115],"In":[90],"paper,":[92],"we":[93,121,152],"demonstrate":[94],"generalizable":[96],"wrapper":[98],"memory":[100,118],"pruning":[101,119],"and":[102,113,168],"bounded":[103],"optimization,":[104],"capable":[105],"being":[107],"used":[108],"any":[110],"acquisition":[114,171],"Using":[116],"approach,":[120],"show":[122],"decrease":[124],"wall-clock":[126],"per":[129],"experiment":[130],"polynomially":[135],"increasing":[136],"pattern":[137,141],"sawtooth":[140],"has":[143],"non-increasing":[145],"trend":[146],"without":[147],"sacrificing":[148],"convergence":[149],"performance.":[150],"Furthermore,":[151],"illustrate":[153],"generalizability":[155],"approach":[158],"across":[159],"two":[160,164],"unique":[161,165,170],"sets,":[163],"models,":[167],"four":[169],"functions.":[172],"All":[173],"implementations":[175],"run":[177],"MIT":[180],"Supercloud":[181],"state-of-the-art":[182],"hardware.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}