{"id":"https://openalex.org/W7162694174","doi":"https://doi.org/10.48550/arxiv.2605.28165","title":"Unification and Optimization of Robust Supervised Learning","display_name":"Unification and Optimization of Robust Supervised Learning","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162694174","doi":"https://doi.org/10.48550/arxiv.2605.28165"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28165","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.28165","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053390198","display_name":"Jonas Hanselle","orcid":"https://orcid.org/0000-0002-1231-4985"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hanselle, Jonas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099566269","display_name":"Valentin Margraf","orcid":"https://orcid.org/0009-0000-5026-044X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Margraf, Valentin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062341746","display_name":"Clemens Damke","orcid":"https://orcid.org/0000-0002-0455-0048"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Damke, Clemens","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137278342","display_name":"Eyke H\u00fcllermeier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"H\u00fcllermeier, Eyke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9595999717712402,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9595999717712402,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.005200000014156103,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.003800000064074993,"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/hyperparameter","display_name":"Hyperparameter","score":0.7095000147819519},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.6173999905586243},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.47029998898506165},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.4205000102519989},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.42010000348091125},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.40369999408721924},{"id":"https://openalex.org/keywords/empirical-risk-minimization","display_name":"Empirical risk minimization","score":0.3865000009536743},{"id":"https://openalex.org/keywords/commit","display_name":"Commit","score":0.36719998717308044},{"id":"https://openalex.org/keywords/unification","display_name":"Unification","score":0.3626999855041504}],"concepts":[{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.7095000147819519},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6847000122070312},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.6173999905586243},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6119999885559082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5885999798774719},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.47029998898506165},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.4205000102519989},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42010000348091125},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.40369999408721924},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.396699994802475},{"id":"https://openalex.org/C107321475","wikidata":"https://www.wikidata.org/wiki/Q5374254","display_name":"Empirical risk minimization","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C153180980","wikidata":"https://www.wikidata.org/wiki/Q19776675","display_name":"Commit","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C96146094","wikidata":"https://www.wikidata.org/wiki/Q609057","display_name":"Unification","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3294999897480011},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3188999891281128},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C66283442","wikidata":"https://www.wikidata.org/wiki/Q1389268","display_name":"Failure mode and effects analysis","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C86941820","wikidata":"https://www.wikidata.org/wiki/Q6865391","display_name":"Minimisation (clinical trials)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2597000002861023},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28165","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.28165","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28165","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"literature":[1],"has":[2],"proposed":[3],"various":[4],"robust":[5,27,89],"alternatives":[6],"to":[7,11,46,50,133],"empirical":[8],"risk":[9,32],"minimisation":[10],"address":[12,66],"failure":[13,53,171],"modes":[14],"such":[15,37],"as":[16],"distribution":[17,95],"shift,":[18],"label":[19,29],"noise":[20],"and":[21,34,81,101,128,141],"finite-sample":[22],"degeneracies.":[23],"Examples":[24],"include":[25],"distributionally":[26],"optimization,":[28],"smoothing,":[30],"vicinal":[31],"minimization,":[33],"Mixup.":[35],"However,":[36],"approaches":[38],"are":[39],"typically":[40],"developed":[41],"in":[42,116,121,155],"isolation,":[43],"forcing":[44],"practitioners":[45,163],"commit":[47],"a":[48,51,70,83,106,117,159,168],"priori":[49,169],"single":[52],"mode":[54,59,172],"even":[55],"when":[56],"the":[57,61,134,151],"dominant":[58],"for":[60,162],"task":[62,135],"is":[63,148],"unclear.":[64],"To":[65],"this,":[67],"we":[68],"organize":[69],"broad":[71],"class":[72],"of":[73,108],"existing":[74],"methods":[75],"along":[76],"three":[77],"common":[78],"design":[79,119],"axes":[80],"derive":[82],"tractable":[84],"training":[85],"procedure":[86],"that":[87],"decomposes":[88],"learning":[90],"into":[91],"sequential":[92],"stages":[93],"(reference":[94],"enrichment,":[96],"input-space":[97],"perturbation,":[98,100],"label-space":[99],"sample-level":[102],"aggregation),":[103],"each":[104,156],"with":[105,150],"choice":[107],"stance":[109],"(pessimistic,":[110],"neutral,":[111],"or":[112],"optimistic).":[113],"This":[114],"results":[115],"unified":[118],"space":[120],"which":[122,170],"joint":[123,145],"hyperparameter":[124,146],"optimization":[125,147],"can":[126],"compose":[127],"configure":[129],"robustness":[130],"strategies":[131],"suited":[132],"at":[136],"hand.":[137],"Across":[138],"tabular,":[139],"image,":[140],"reward":[142],"modeling":[143],"benchmarks,":[144],"competitive":[149],"best":[152],"single-method":[153],"baseline":[154],"setting,":[157],"offering":[158],"reliable":[160],"default":[161],"who":[164],"do":[165],"not":[166],"know":[167],"dominates":[173],"their":[174],"task.":[175]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-29T00:00:00"}
