{"id":"https://openalex.org/W7160825223","doi":"https://doi.org/10.48550/arxiv.2605.07557","title":"Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning","display_name":"Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160825223","doi":"https://doi.org/10.48550/arxiv.2605.07557"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07557","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07557","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.07557","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135842278","display_name":"Yaxin Hou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hou, Yaxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135908183","display_name":"Jun Ma","orcid":"https://orcid.org/0000-0002-6127-000X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135838889","display_name":"Hanyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hanyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135830492","display_name":"Bo Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135889856","display_name":"Jie Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135876551","display_name":"Yuheng Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Yuheng","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":0.6373000144958496,"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":0.6373000144958496,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2087000012397766,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.036400001496076584,"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/simplex","display_name":"Simplex","score":0.7057999968528748},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6549999713897705},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5806000232696533},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5773000121116638},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5264000296592712},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5092999935150146},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.4440999925136566},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4300000071525574}],"concepts":[{"id":"https://openalex.org/C62438384","wikidata":"https://www.wikidata.org/wiki/Q331350","display_name":"Simplex","level":2,"score":0.7057999968528748},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6549999713897705},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5806000232696533},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5773000121116638},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5450999736785889},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5264000296592712},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5092999935150146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46810001134872437},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4300000071525574},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.40959998965263367},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38339999318122864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34700000286102295},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.3319999873638153},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3296000063419342},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3264000117778778},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C144521790","wikidata":"https://www.wikidata.org/wiki/Q134164","display_name":"Simplex algorithm","level":3,"score":0.30649998784065247},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07557","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07557","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.07557","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07557","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":"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":{"Semi-supervised":[0,29],"learning":[1],"faces":[2],"significant":[3],"challenges":[4],"in":[5],"realistic":[6],"scenarios":[7],"where":[8],"labeled":[9,57],"data":[10,15,37,52,58],"is":[11],"scarce":[12],"and":[13,167],"unlabeled":[14,36,51],"follows":[16],"unknown,":[17],"arbitrary":[18],"distributions.":[19],"We":[20],"formalize":[21],"this":[22,92],"critical":[23],"yet":[24],"under-explored":[25],"paradigm":[26],"as":[27,144],"Universal":[28],"Learning":[30],"(UniSSL).":[31],"Existing":[32],"methods":[33],"typically":[34],"leverage":[35],"via":[38],"pseudo-labeling.":[39],"However,":[40],"they":[41],"often":[42],"rely":[43],"on":[44,160,177],"the":[45,63],"idealized":[46],"assumption":[47],"of":[48,193],"a":[49,137,145,156],"uniform":[50],"distribution":[53,104],"or":[54],"require":[55],"sufficient":[56],"to":[59,69,98,102,119,128,142,163,171],"estimate":[60],"it.":[61],"In":[62],"UniSSL":[64],"setting,":[65],"such":[66],"dependencies":[67,118],"lead":[68],"numerous":[70],"erroneous":[71,174],"pseudo-labels,":[72],"thereby":[73],"triggering":[74],"representation":[75,125,130,151],"confusion.":[76],"Fortunately,":[77],"we":[78,94,107,132,154],"observe":[79],"that":[80,135,182],"inter-sample":[81,117],"relations":[82],"captured":[83],"by":[84],"representations":[85],"are":[86],"more":[87],"reliable":[88,165],"than":[89],"pseudo-labels.":[90,175],"Leveraging":[91],"insight,":[93],"shift":[95],"our":[96],"focus":[97],"representation-level":[99],"structural":[100,121],"inference":[101],"bypass":[103],"estimation.":[105],"Accordingly,":[106],"propose":[108],"Simplex":[109],"Anchored":[110],"Graph-state":[111],"Equipartition":[112],"(SAGE),":[113],"which":[114],"captures":[115],"high-order":[116],"establish":[120],"consensus":[122],"for":[123,148],"guiding":[124,149],"learning.":[126],"Meanwhile,":[127],"mitigate":[129],"confusion,":[131],"employ":[133],"vectors":[134],"satisfy":[136],"simplex":[138],"equiangular":[139],"tight":[140],"frame":[141,147],"serve":[143],"coordinate":[146],"inter-class":[150],"separation.":[152],"Finally,":[153],"introduce":[155],"weighting":[157],"strategy":[158],"based":[159],"distribution-agnostic":[161],"metrics":[162],"prioritize":[164],"pseudo-labels":[166],"an":[168,189],"auxiliary":[169],"branch":[170],"isolate":[172],"potentially":[173],"Evaluations":[176],"five":[178],"standard":[179],"benchmarks":[180],"show":[181],"SAGE":[183],"consistently":[184],"outperforms":[185],"state-of-the-art":[186],"methods,":[187],"with":[188],"average":[190],"accuracy":[191],"gain":[192],"$\\textbf{8.52%}$.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-12T00:00:00"}
