{"id":"https://openalex.org/W7140317495","doi":"https://doi.org/10.48550/arxiv.2603.22548","title":"L2O-CCG: Adversarial Learning with Set Generalization for Adaptive Robust Optimization","display_name":"L2O-CCG: Adversarial Learning with Set Generalization for Adaptive Robust Optimization","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140317495","doi":"https://doi.org/10.48550/arxiv.2603.22548"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.22548","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22548","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.22548","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130563976","display_name":"Zhiyi Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Zhiyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121146895","display_name":"Jan Drgona","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Drgo\u0148a, J\u00e1n","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130561495","display_name":"Yury Dvorkin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dvorkin, Yury","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.4668999910354614,"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":0.4668999910354614,"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/T11413","display_name":"Risk and Portfolio Optimization","score":0.2078000009059906,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.055799998342990875,"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/adversarial-system","display_name":"Adversarial system","score":0.6230000257492065},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6169000267982483},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5810999870300293},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.546500027179718},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.5314000248908997},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5062000155448914},{"id":"https://openalex.org/keywords/robust-optimization","display_name":"Robust optimization","score":0.4867999851703644},{"id":"https://openalex.org/keywords/trust-region","display_name":"Trust region","score":0.42730000615119934}],"concepts":[{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6281999945640564},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6230000257492065},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6169000267982483},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5810999870300293},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.5314000248908997},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5062000155448914},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5013999938964844},{"id":"https://openalex.org/C193254401","wikidata":"https://www.wikidata.org/wiki/Q2160088","display_name":"Robust optimization","level":2,"score":0.4867999851703644},{"id":"https://openalex.org/C89109886","wikidata":"https://www.wikidata.org/wiki/Q1535924","display_name":"Trust region","level":3,"score":0.42730000615119934},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4198000133037567},{"id":"https://openalex.org/C15312841","wikidata":"https://www.wikidata.org/wiki/Q1657198","display_name":"Set function","level":3,"score":0.39570000767707825},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.364300012588501},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35260000824928284},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3434000015258789},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2621000111103058}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.22548","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22548","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":"doi:10.48550/arxiv.2603.22548","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22548","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":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"adversarial":[1,87,102,108],"subproblem":[2],"in":[3],"two-stage":[4],"adaptive":[5],"robust":[6],"optimization":[7],"(ARO),":[8],"which":[9,105],"identifies":[10],"the":[11,25,32,46,50,66,83,90,131,165,169,175,183],"worst-case":[12],"uncertainty":[13,33,51,120,159,176],"realization,":[14],"is":[15,22,29,172],"a":[16,78,100,111,148,187],"major":[17],"computational":[18],"bottleneck.":[19],"This":[20],"difficulty":[21],"exacerbated":[23],"when":[24,60],"recourse":[26,132],"value":[27,47,133],"function":[28,48,134],"non-concave":[30],"and":[31],"set":[34,52,121,160,177],"shifts":[35],"across":[36,119],"applications.":[37],"Existing":[38],"approaches":[39],"typically":[40],"exploit":[41],"specific":[42],"structural":[43],"assumptions":[44,62],"on":[45,186],"or":[49,65],"geometry":[53,67],"to":[54],"reformulate":[55],"this":[56,73],"subproblem,":[57],"but":[58],"degrade":[59],"these":[61],"are":[63],"violated":[64],"changes":[68],"at":[69],"deployment.":[70],"To":[71],"address":[72],"challenge,":[74],"we":[75,98],"propose":[76],"L2O-CCG,":[77],"bi-level":[79],"framework":[80],"that":[81,116],"enables":[82],"integration":[84],"of":[85,168,182],"structure-aware":[86],"solvers":[88],"within":[89],"constraint-and-column":[91],"generation":[92],"(CCG)":[93],"algorithm.":[94],"As":[95],"one":[96],"instantiation,":[97],"develop":[99],"generalizable":[101],"learning":[103],"method,":[104],"replaces":[106],"solver-based":[107],"search":[109],"with":[110],"learned":[112,170],"proximal":[113,149],"gradient":[114,150],"optimizer":[115,171],"can":[117],"generalize":[118],"geometries":[122],"without":[123],"retraining.":[124],"Here,":[125],"an":[126,139],"inner-level":[127],"neural":[128],"network":[129],"approximates":[130],"from":[135],"offline":[136],"data,":[137],"while":[138],"outer-level":[140],"pre-trained":[141],"mapping":[142],"generates":[143],"iteration-dependent":[144],"step":[145],"sizes":[146],"for":[147],"scheme.":[151],"We":[152,179],"also":[153],"establish":[154],"out-of-distribution":[155],"convergence":[156],"bounds":[157],"under":[158],"parameter":[161],"shifts,":[162],"showing":[163],"how":[164],"trajectory":[166],"deviation":[167],"bounded":[173],"by":[174],"shift.":[178],"illustrate":[180],"performance":[181],"L2O-CCG":[184],"method":[185],"building":[188],"HVAC":[189],"management":[190],"task.":[191]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-26T00:00:00"}