{"id":"https://openalex.org/W7163003862","doi":"https://doi.org/10.48550/arxiv.2605.30997","title":"Hedging on the Frontier: Learning New Tasks with Few Samples","display_name":"Hedging on the Frontier: Learning New Tasks with Few Samples","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163003862","doi":"https://doi.org/10.48550/arxiv.2605.30997"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30997","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.30997","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013986944","display_name":"Tobias Wegel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wegel, Tobias","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120599149","display_name":"Federico Di Gennaro","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Gennaro, Federico","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055116842","display_name":"Geelon So","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"So, Geelon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5113814271","display_name":"Fanny Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Fanny","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5246000289916992,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5246000289916992,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.10360000282526016,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.09430000185966492,"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/leverage","display_name":"Leverage (statistics)","score":0.7631999850273132},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6018000245094299},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4993000030517578},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.47440001368522644},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4424999952316284},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.42910000681877136},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.39879998564720154}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7631999850273132},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.629800021648407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.607699990272522},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6018000245094299},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5827000141143799},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.47440001368522644},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4424999952316284},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.39879998564720154},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3555000126361847},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C24138899","wikidata":"https://www.wikidata.org/wiki/Q17141258","display_name":"Instance-based learning","level":3,"score":0.314300000667572},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.30869999527931213},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30997","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.30997","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30997","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"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":{"When":[0],"a":[1,4,69],"learner":[2],"faces":[3],"new":[5,83],"task":[6,42],"with":[7],"few":[8],"samples,":[9],"it":[10,46,76,97],"must":[11],"leverage":[12],"any":[13],"available":[14,133],"side":[15],"information.":[16],"In":[17],"practice,":[18],"this":[19],"often":[20,65],"comes":[21],"in":[22,31],"the":[23,51,82,87,116,129,132,138],"form":[24],"of":[25,90,131],"model":[26,41,70,105,117],"evaluations":[27,53],"on":[28,73,81,120,137],"related":[29],"tasks":[30],"public":[32],"benchmarks.":[33],"A":[34],"key":[35],"question":[36],"then":[37],"is":[38,47,64],"how":[39],"to":[40,55,79,128],"relatedness":[43],"such":[44],"that":[45,61,110],"both":[48],"realistic":[49],"and":[50,104],"benchmark":[52],"lead":[54],"provable":[56],"gains.":[57],"Empirically,":[58],"we":[59,114,123],"observe":[60],"weak":[62,94],"monotonicity":[63],"approximately":[66],"satisfied:":[67],"if":[68],"dominates":[71],"another":[72],"many":[74],"benchmarks,":[75],"also":[77,125],"tends":[78],"outperform":[80],"task.":[84],"We":[85,108],"explore":[86],"statistical":[88],"complexity":[89],"learning":[91,100,103],"under":[92],"(approximate)":[93],"monotonicity,":[95,121],"leveraging":[96],"within":[98],"two":[99],"paradigms:":[101],"transfer":[102],"selection":[106],"aggregation.":[107],"show":[109],"not":[111],"only":[112],"can":[113,124],"prune":[115],"class":[118],"based":[119],"but":[122],"further":[126],"adapt":[127],"geometry":[130],"trade-offs":[134],"by":[135],"hedging":[136],"frontier.":[139]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-06-02T00:00:00"}