{"id":"https://openalex.org/W6966657726","doi":"https://doi.org/10.48550/arxiv.2506.18629","title":"On Equivariant Model Selection through the Lens of Uncertainty","display_name":"On Equivariant Model Selection through the Lens of Uncertainty","publication_year":2025,"publication_date":"2025-06-23","ids":{"openalex":"https://openalex.org/W6966657726","doi":"https://doi.org/10.48550/arxiv.2506.18629"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2506.18629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.18629","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.2506.18629","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"van der Linden, Putri A.","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"van der Linden, Putri A.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Timans, Alexander","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Timans, Alexander","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Tailor, Dharmesh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tailor, Dharmesh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Bekkers, Erik J.","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bekkers, Erik J.","raw_affiliation_strings":[],"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":true,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.20550000667572021,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.20550000667572021,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.14569999277591705,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.12430000305175781,"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/frequentist-inference","display_name":"Frequentist inference","score":0.6238999962806702},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5920000076293945},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5496000051498413},{"id":"https://openalex.org/keywords/equivariant-map","display_name":"Equivariant map","score":0.5221999883651733},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5063999891281128},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.3887999951839447},{"id":"https://openalex.org/keywords/bayesian-statistics","display_name":"Bayesian statistics","score":0.3871999979019165},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.3587999939918518},{"id":"https://openalex.org/keywords/bayesian-experimental-design","display_name":"Bayesian experimental design","score":0.33079999685287476}],"concepts":[{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.6238999962806702},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5920000076293945},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5496000051498413},{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.5221999883651733},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5063999891281128},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46309998631477356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43130001425743103},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.426800012588501},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41350001096725464},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3887999951839447},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.3871999979019165},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C99173435","wikidata":"https://www.wikidata.org/wiki/Q4874469","display_name":"Bayesian experimental design","level":5,"score":0.33079999685287476},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.31310001015663147},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C43091099","wikidata":"https://www.wikidata.org/wiki/Q1067788","display_name":"Through-the-lens metering","level":3,"score":0.2930000126361847},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C37903108","wikidata":"https://www.wikidata.org/wiki/Q4874474","display_name":"Bayesian linear regression","level":4,"score":0.2825999855995178},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2506.18629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.18629","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.2506.18629","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.18629","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":{"Equivariant":[0],"models":[1,30],"leverage":[2],"prior":[3],"knowledge":[4],"on":[5],"symmetries":[6],"to":[7,60,84],"improve":[8],"predictive":[9,72],"performance,":[10,73],"but":[11,74],"misspecified":[12],"architectural":[13],"constraints":[14],"can":[15],"harm":[16],"it":[17],"instead.":[18],"While":[19],"work":[20],"has":[21],"explored":[22],"learning":[23],"or":[24],"relaxing":[25],"constraints,":[26],"selecting":[27],"among":[28],"pretrained":[29],"with":[31,71],"varying":[32],"symmetry":[33],"biases":[34],"remains":[35],"challenging.":[36],"We":[37,64,81],"examine":[38],"this":[39,83],"model":[40,76,93,116],"selection":[41],"task":[42],"from":[43],"an":[44],"uncertainty-aware":[45],"perspective,":[46],"comparing":[47],"frequentist":[48],"(via":[49,53],"Conformal":[50],"Prediction),":[51],"Bayesian":[52,75,88],"the":[54,96,109],"marginal":[55],"likelihood),":[56],"and":[57,89,101],"calibration-based":[58],"measures":[59],"naive":[61],"error-based":[62],"evaluation.":[63],"find":[65],"that":[66],"uncertainty":[67,112],"metrics":[68],"generally":[69],"align":[70],"evidence":[77],"does":[78],"so":[79],"inconsistently.":[80],"attribute":[82],"a":[85],"mismatch":[86],"in":[87,113],"geometric":[90],"notions":[91],"of":[92,111],"complexity":[94],"for":[95],"employed":[97],"last-layer":[98],"Laplace":[99],"approximation,":[100],"discuss":[102],"possible":[103],"remedies.":[104],"Our":[105],"findings":[106],"point":[107],"towards":[108],"potential":[110],"guiding":[114],"symmetry-aware":[115],"selection.":[117]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}