{"id":"https://openalex.org/W7131818558","doi":"https://doi.org/10.48550/arxiv.2602.22422","title":"Revisiting Chebyshev Polynomial and Anisotropic RBF Models for Tabular Regression","display_name":"Revisiting Chebyshev Polynomial and Anisotropic RBF Models for Tabular Regression","publication_year":2026,"publication_date":"2026-02-25","ids":{"openalex":"https://openalex.org/W7131818558","doi":"https://doi.org/10.48550/arxiv.2602.22422"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.22422","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126989895","display_name":"Luciano Gerber","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gerber, Luciano","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127299004","display_name":"Huw Lloyd","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lloyd, Huw","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5126989895"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.09589999914169312,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.09589999914169312,"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/T12303","display_name":"Tensor decomposition and applications","score":0.07010000199079514,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.05209999904036522,"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/differentiable-function","display_name":"Differentiable function","score":0.5210000276565552},{"id":"https://openalex.org/keywords/chebyshev-polynomials","display_name":"Chebyshev polynomials","score":0.5206000208854675},{"id":"https://openalex.org/keywords/polynomial","display_name":"Polynomial","score":0.5038999915122986},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5012000203132629},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48190000653266907},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.4611999988555908},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4526999890804291},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.43050000071525574}],"concepts":[{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.5210000276565552},{"id":"https://openalex.org/C129785596","wikidata":"https://www.wikidata.org/wiki/Q619511","display_name":"Chebyshev polynomials","level":2,"score":0.5206000208854675},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.5205000042915344},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.5038999915122986},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5012000203132629},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.49079999327659607},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48190000653266907},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4706999957561493},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.4611999988555908},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4526999890804291},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.4198000133037567},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4198000133037567},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40450000762939453},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.34700000286102295},{"id":"https://openalex.org/C21424316","wikidata":"https://www.wikidata.org/wiki/Q718621","display_name":"Chebyshev filter","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C98856871","wikidata":"https://www.wikidata.org/wiki/Q1588488","display_name":"Radial basis function","level":3,"score":0.335999995470047},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3273000121116638},{"id":"https://openalex.org/C171326582","wikidata":"https://www.wikidata.org/wiki/Q1052508","display_name":"Chebyshev nodes","level":3,"score":0.2978000044822693},{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2630999982357025},{"id":"https://openalex.org/C145242015","wikidata":"https://www.wikidata.org/wiki/Q774123","display_name":"Approximation theory","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C138029060","wikidata":"https://www.wikidata.org/wiki/Q7226633","display_name":"Polynomial and rational function modeling","level":3,"score":0.2590999901294708}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.22422","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.22422","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.22422","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.22422","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.5577176213264465,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Smooth-basis":[0],"models":[1,43,60,155,178],"such":[2],"as":[3,99],"Chebyshev":[4,85],"polynomial":[5,86],"regressors":[6],"and":[7,29,79,88,111,136,149,156,191],"radial":[8],"basis":[9],"function":[10],"(RBF)":[11],"networks":[12],"are":[13,97,159],"well":[14],"established":[15],"in":[16,46,141,179],"numerical":[17],"analysis.":[18],"Their":[19],"continuously":[20],"differentiable":[21],"prediction":[22],"surfaces":[23],"suit":[24],"surrogate":[25],"optimisation,":[26,82],"sensitivity":[27],"analysis,":[28],"other":[30],"settings":[31,144],"where":[32,49],"the":[33,142,165,180],"response":[34],"varies":[35],"gradually":[36,192],"with":[37,75],"inputs.":[38],"Despite":[39],"these":[40,104],"properties,":[41],"smooth":[42,154],"seldom":[44],"appear":[45],"tabular":[47],"regression,":[48],"tree":[50,106,157],"ensembles":[51,158],"dominate.":[52],"We":[53,69,102,173],"ask":[54],"whether":[55],"they":[56],"can":[57],"compete,":[58],"benchmarking":[59],"across":[61,125,146],"55":[62],"regression":[63],"datasets":[64],"organised":[65],"by":[66],"application":[67],"domain.":[68],"develop":[70],"an":[71],"anisotropic":[72],"RBF":[73],"network":[74],"data-driven":[76],"centre":[77],"placement":[78],"gradient-based":[80],"width":[81],"a":[83,89,108,126],"ridge-regularised":[84],"regressor,":[87],"smooth-tree":[90],"hybrid":[91],"(Chebyshev":[92],"model":[93],"tree);":[94],"all":[95],"three":[96],"released":[98],"scikit-learn-compatible":[100],"packages.":[101],"benchmark":[103],"against":[105],"ensembles,":[107],"pre-trained":[109],"transformer,":[110],"standard":[112],"baselines,":[113],"evaluating":[114],"accuracy":[115,124],"alongside":[116],"generalisation":[117,171,190],"behaviour.":[118],"The":[119],"transformer":[120],"ranks":[121],"first":[122],"on":[123,162],"majority":[127],"of":[128],"datasets,":[129],"but":[130,164],"its":[131],"GPU":[132],"dependence,":[133],"inference":[134],"latency,":[135],"dataset-size":[137],"limits":[138],"constrain":[139],"deployment":[140],"CPU-based":[143],"common":[145],"applied":[147],"science":[148],"industry.":[150],"Among":[151],"CPU-viable":[152],"models,":[153],"statistically":[160],"tied":[161],"accuracy,":[163],"former":[166],"tend":[167],"to":[168],"exhibit":[169],"tighter":[170,189],"gaps.":[172],"recommend":[174],"routinely":[175],"including":[176],"smooth-basis":[177],"candidate":[181],"pool,":[182],"particularly":[183],"when":[184],"downstream":[185],"use":[186],"benefits":[187],"from":[188],"varying":[193],"predictions.":[194]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-28T00:00:00"}