{"id":"https://openalex.org/W4412877241","doi":"https://doi.org/10.1145/3711896.3737607","title":"Boost the Performance of Tabular Data Models with GPU Accelerated Feature Engineering","display_name":"Boost the Performance of Tabular Data Models with GPU Accelerated Feature Engineering","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412877241","doi":"https://doi.org/10.1145/3711896.3737607"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737607","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737607","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737607","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737607","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026940220","display_name":"Chris Deotte","orcid":null},"institutions":[{"id":"https://openalex.org/I1304085615","display_name":"Nvidia (United Kingdom)","ror":"https://ror.org/02kr42612","country_code":"GB","type":"company","lineage":["https://openalex.org/I1304085615","https://openalex.org/I4210127875"]},{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["GB","US"],"is_corresponding":true,"raw_author_name":"Chris Deotte","raw_affiliation_strings":["NVIDIA, San Diego, CA, USA"],"affiliations":[{"raw_affiliation_string":"NVIDIA, San Diego, CA, USA","institution_ids":["https://openalex.org/I4210127875","https://openalex.org/I1304085615"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110359515","display_name":"Ronay Ak","orcid":"https://orcid.org/0000-0003-2768-6535"},"institutions":[{"id":"https://openalex.org/I4210127875","display_name":"Nvidia (United States)","ror":"https://ror.org/03jdj4y14","country_code":"US","type":"company","lineage":["https://openalex.org/I4210127875"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ronay Ak","raw_affiliation_strings":["NVIDIA, Sarasota, FL, USA"],"affiliations":[{"raw_affiliation_string":"NVIDIA, Sarasota, FL, USA","institution_ids":["https://openalex.org/I4210127875"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5026940220"],"corresponding_institution_ids":["https://openalex.org/I1304085615","https://openalex.org/I4210127875"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10565725,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6237","last_page":"6238"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11269","display_name":"Algorithms and Data Compression","score":0.9973000288009644,"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/T11269","display_name":"Algorithms and Data Compression","score":0.9973000288009644,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7518718242645264},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5822797417640686},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.524293839931488},{"id":"https://openalex.org/keywords/computational-science","display_name":"Computational science","score":0.46282392740249634},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.437873512506485},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.4120042324066162},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3873569071292877},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3536418080329895},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.13665392994880676}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7518718242645264},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5822797417640686},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.524293839931488},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.46282392740249634},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.437873512506485},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.4120042324066162},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3873569071292877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3536418080329895},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.13665392994880676},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3711896.3737607","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737607","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737607","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3711896.3737607","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737607","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737607","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4412877241.pdf"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W2027731328","https://openalex.org/W3088097218","https://openalex.org/W3215469691"],"related_works":["https://openalex.org/W2005148983","https://openalex.org/W2012954338","https://openalex.org/W2096672917","https://openalex.org/W2392023973","https://openalex.org/W3189307731","https://openalex.org/W1428699136","https://openalex.org/W2949962288","https://openalex.org/W2750075801","https://openalex.org/W2364686214","https://openalex.org/W1998560227"],"abstract_inverted_index":{"Feature":[0],"engineering":[1],"remains":[2],"a":[3],"crucial":[4],"technique":[5],"for":[6],"improving":[7],"the":[8,34],"performance":[9],"of":[10],"models":[11,26],"trained":[12],"on":[13],"tabular":[14,37],"data.":[15],"Unlike":[16],"computer":[17],"vision":[18],"and":[19],"natural":[20],"language":[21],"processing,":[22],"where":[23],"deep":[24],"learning":[25],"automatically":[27],"extract":[28],"hierarchical":[29],"features":[30],"from":[31,48],"raw":[32],"data,":[33],"most":[35],"accurate":[36],"models,":[38],"such":[39],"as":[40],"gradient":[41],"boosted":[42],"decision":[43],"trees,":[44],"still":[45],"benefit":[46],"significantly":[47],"manually":[49],"crafted":[50],"features.":[51],"This":[52],"is":[53],"demonstrated":[54],"in":[55],"Team":[56],"NVIDIA's":[57],"many":[58],"first-place":[59],"data":[60],"science":[61],"competition":[62],"victories.":[63]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
