{"id":"https://openalex.org/W4281638598","doi":"https://doi.org/10.1145/3534678.3539278","title":"Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction","display_name":"Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4281638598","doi":"https://doi.org/10.1145/3534678.3539278"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539278","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539278","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5101737969","display_name":"Dongjie Wang","orcid":"https://orcid.org/0000-0003-3948-0059"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongjie Wang","raw_affiliation_strings":["University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032187620","display_name":"Yanjie Fu","orcid":"https://orcid.org/0000-0002-1767-8024"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanjie Fu","raw_affiliation_strings":["University of Central Florida, Orlando, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida, Orlando, FL, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100786547","display_name":"Kunpeng Liu","orcid":"https://orcid.org/0000-0002-6053-5977"},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kunpeng Liu","raw_affiliation_strings":["Portland State University, Portland, OR, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Portland State University, Portland, OR, USA","institution_ids":["https://openalex.org/I126345244"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353796","display_name":"Xiaolin Li","orcid":"https://orcid.org/0000-0002-0997-2776"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaolin Li","raw_affiliation_strings":["Nanjing Univerisity, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Univerisity, Nanjing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061775189","display_name":"Yan Solihin","orcid":"https://orcid.org/0000-0002-8863-941X"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Solihin","raw_affiliation_strings":["University of Central Florida, Orlando , FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida, Orlando , FL, USA","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1826","last_page":"1834"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","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/T11689","display_name":"Adversarial Robustness in Machine Learning","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9993000030517578,"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":0.9986000061035156,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7116589546203613},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6509872674942017},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6214035749435425},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6150923371315002},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.593376636505127},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5298095941543579},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5025315284729004},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.49665361642837524},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4744837284088135},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.44833096861839294},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.41193562746047974},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3410238027572632},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.20593488216400146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15334203839302063}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7116589546203613},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6509872674942017},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6214035749435425},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6150923371315002},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.593376636505127},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5298095941543579},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5025315284729004},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.49665361642837524},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4744837284088135},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.44833096861839294},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.41193562746047974},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3410238027572632},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.20593488216400146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15334203839302063},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539278","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539278","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.6700000166893005}],"awards":[{"id":"https://openalex.org/G3816923980","display_name":null,"funder_award_id":"1755946, 2040950, 2006889, 2045567","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W2135046866","https://openalex.org/W2145962650","https://openalex.org/W2552025135","https://openalex.org/W2560674852","https://openalex.org/W2564702731","https://openalex.org/W2612872092","https://openalex.org/W2746553466","https://openalex.org/W2759903677","https://openalex.org/W2977715335","https://openalex.org/W2998216295","https://openalex.org/W3124185882","https://openalex.org/W4224316956","https://openalex.org/W4225729154","https://openalex.org/W4288072853","https://openalex.org/W4300011764"],"related_works":["https://openalex.org/W4296209631","https://openalex.org/W2561617217","https://openalex.org/W3097449145","https://openalex.org/W4294811468","https://openalex.org/W2355801475","https://openalex.org/W106004901","https://openalex.org/W2786094008","https://openalex.org/W3131501806","https://openalex.org/W2799683370","https://openalex.org/W2807745940"],"abstract_inverted_index":{"Representation":[0],"(feature)":[1],"space":[2,26,89,109,130],"is":[3,27,57,91,145,156],"an":[4,133,207],"environment":[5],"where":[6,142],"data":[7,39],"points":[8],"are":[9,12,15,20],"vectorized,":[10],"distances":[11],"computed,":[13],"patterns":[14],"characterized,":[16],"and":[17,41,54,70,79,83,104,140,150,153,190,192,209,217,226,244],"geometric":[18],"structures":[19],"embedded.":[21],"Extracting":[22],"a":[23,112,122,167,198,204],"good":[24],"representation":[25,55,108,129],"critical":[28],"to":[29,146,157,161,179,184,202,213,238],"address":[30,100],"the":[31,43,101,186,193,219,240],"curse":[32],"of":[33,45,136,188,230,246],"dimensionality,":[34],"improve":[35],"model":[36],"generalization,":[37],"overcome":[38],"sparsity,":[40],"increase":[42],"availability":[44],"classic":[46],"models.":[47],"Existing":[48],"literature,":[49],"such":[50],"as":[51],"feature":[52,88,138,143,154,163,194,205,211],"engineering":[53],"learning,":[56],"limited":[58],"in":[59,107],"achieving":[60],"full":[61],"automation":[62],"(e.g.,":[63,75,86],"over":[64],"heavy":[65],"reliance":[66],"on":[67],"intensive":[68],"labor":[69],"empirical":[71],"experiences),":[72],"explainable":[73,80],"explicitness":[74,245],"traceable":[76],"reconstruction":[77,90,110,131],"process":[78,135],"new":[81,148,215],"features),":[82],"flexible":[84],"optimal":[85,87,105,181],"not":[92],"embedded":[93],"into":[94,132],"downstream":[95],"tasks).":[96],"Can":[97],"we":[98,120,234],"simultaneously":[99],"automation,":[102],"explicitness,":[103],"challenges":[106],"for":[111],"machine":[113],"learning":[114,170],"task?":[115],"To":[116],"answer":[117],"this":[118],"question,":[119],"propose":[121],"group-wise":[123,199],"reinforcement":[124,169],"generation":[125,139,144,182,200],"perspective.":[126],"We":[127,165,196],"reformulate":[128],"interactive":[134],"nested":[137],"selection,":[141],"generate":[147,214],"meaningful":[149],"explicit":[151],"features,":[152],"selection":[155,187],"eliminate":[158],"redundant":[159],"features":[160,189,216],"control":[162],"sizes.":[164],"develop":[166],"cascading":[168,175,231],"method":[171],"that":[172,221],"leverages":[173],"three":[174],"Markov":[176],"Decision":[177],"Processes":[178],"learn":[180],"policies":[183],"automate":[185],"operations":[191],"crossing.":[195],"design":[197],"strategy":[201,220],"cross":[203],"group,":[206],"operation,":[208],"another":[210],"group":[212],"find":[218],"can":[222],"enhance":[223],"exploration":[224],"efficiency":[225],"augment":[227],"reward":[228],"signals":[229],"agents.":[232],"Finally,":[233],"present":[235],"extensive":[236],"experiments":[237],"demonstrate":[239],"effectiveness,":[241],"efficiency,":[242],"traceability,":[243],"our":[247],"system.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}