{"id":"https://openalex.org/W4385565490","doi":"https://doi.org/10.1145/3580305.3599474","title":"Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization","display_name":"Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385565490","doi":"https://doi.org/10.1145/3580305.3599474"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599474","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5059136372","display_name":"Long He","orcid":"https://orcid.org/0000-0002-9951-7450"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Long He","raw_affiliation_strings":["George Washington University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"George Washington University, Washington, DC, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070927930","display_name":"Ho\u2010Yin Mak","orcid":"https://orcid.org/0000-0002-4289-8029"},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ho-Yin Mak","raw_affiliation_strings":["Georgetown University, Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"Georgetown University, Washington, DC, USA","institution_ids":["https://openalex.org/I184565670"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5059136372"],"corresponding_institution_ids":["https://openalex.org/I193531525"],"apc_list":null,"apc_paid":null,"fwci":0.7636,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74448051,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"711","last_page":"721"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.9922000169754028,"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"}},"topics":[{"id":"https://openalex.org/T11413","display_name":"Risk and Portfolio Optimization","score":0.9922000169754028,"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"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12617","display_name":"Energy, Environment, and Transportation Policies","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.8482701778411865},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.6185430288314819},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.602541983127594},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6006386280059814},{"id":"https://openalex.org/keywords/stochastic-programming","display_name":"Stochastic programming","score":0.5925461649894714},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5655236840248108},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.5443971753120422},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.5124695897102356},{"id":"https://openalex.org/keywords/robust-optimization","display_name":"Robust optimization","score":0.48886793851852417},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4559791684150696},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4557982087135315},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30217427015304565},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27599388360977173},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22716030478477478}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.8482701778411865},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6185430288314819},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.602541983127594},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6006386280059814},{"id":"https://openalex.org/C137631369","wikidata":"https://www.wikidata.org/wiki/Q7617831","display_name":"Stochastic programming","level":2,"score":0.5925461649894714},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5655236840248108},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.5443971753120422},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.5124695897102356},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.48886793851852417},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4559791684150696},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4557982087135315},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30217427015304565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27599388360977173},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22716030478477478},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599474","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599474","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2004026774","https://openalex.org/W2014923785","https://openalex.org/W2069737635","https://openalex.org/W2078135889","https://openalex.org/W2078355481","https://openalex.org/W2144629557","https://openalex.org/W2344697147","https://openalex.org/W2600094625","https://openalex.org/W2607963324","https://openalex.org/W2782362620","https://openalex.org/W2800812001","https://openalex.org/W2915057847","https://openalex.org/W2952736586","https://openalex.org/W2969714402","https://openalex.org/W2994843702","https://openalex.org/W2998004193","https://openalex.org/W3023550848","https://openalex.org/W3122167207","https://openalex.org/W3136027957","https://openalex.org/W4238714550","https://openalex.org/W4287779368"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W4391160746","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241"],"abstract_inverted_index":{"In":[0,38],"this":[1,39,83],"paper,":[2],"we":[3,85,118],"consider":[4],"the":[5,58,64,71,77,96,101,105,108],"alignment":[6],"between":[7],"an":[8,114],"upstream":[9],"dimensionality":[10,42,89,122],"reduction":[11,43,90,123],"task":[12,28],"of":[13,18,21,29,60,73,98],"learning":[14],"a":[15,19,25,31,87,129,135,143,148],"low-dimensional":[16],"representation":[17,65],"set":[20],"high-dimensional":[22],"data":[23,165],"and":[24,66,147,163],"downstream":[26,78,109],"optimization":[27,79,102,111,131],"solving":[30,128],"stochastic":[32,110],"program":[33],"parameterized":[34],"by":[35],"said":[36],"representation.":[37],"case,":[40],"standard":[41],"methods":[44],"(e.g.,":[45],"principal":[46,158],"component":[47,159],"analysis)":[48],"may":[49],"not":[50,68],"perform":[51],"well,":[52],"as":[53],"they":[54],"aim":[55],"to":[56,94],"maximize":[57],"amount":[59],"information":[61,75],"retained":[62],"in":[63,76,100],"do":[67],"generally":[69],"reflect":[70],"importance":[72],"such":[74],"problem.":[80],"To":[81],"address":[82],"problem,":[84,132],"develop":[86],"prescriptive":[88,121],"framework":[91],"that":[92,120,153],"aims":[93],"minimize":[95],"degree":[97],"suboptimality":[99],"phase.":[103],"For":[104],"case":[106],"where":[107],"problem":[112,146,151],"has":[113],"expected":[115],"value":[116],"objective,":[117],"show":[119,152],"can":[124],"be":[125],"performed":[126],"via":[127],"distributionally-robust":[130],"which":[133],"admits":[134],"semidefinite":[136],"programming":[137],"relaxation.":[138],"Computational":[139],"experiments":[140],"based":[141],"on":[142],"warehouse":[144],"transshipment":[145],"vehicle":[149],"repositioning":[150],"our":[154],"approach":[155],"significantly":[156],"outperforms":[157],"analysis":[160],"with":[161],"real":[162],"synthetic":[164],"sets.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}