{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZEHQBNS5B2BWPBADUT5TAVJXSO","short_pith_number":"pith:ZEHQBNS5","schema_version":"1.0","canonical_sha256":"c90f00b65d0e83678403a4fb30553793b47e1b1102828ee90792de1a5c16ca1d","source":{"kind":"arxiv","id":"2409.17267","version":2},"attestation_state":"computed","paper":{"title":"Minimal Variance Model Aggregation: A principled, non-intrusive, and versatile integration of black box models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NA","math.NA","stat.ML"],"primary_cat":"cs.LG","authors_text":"Houman Owhadi, Th\\'eo Bourdais","submitted_at":"2024-09-25T18:33:21Z","abstract_excerpt":"Whether deterministic or stochastic, models can be viewed as functions designed to approximate a specific quantity of interest. We introduce Minimal Empirical Variance Aggregation (MEVA), a data-driven framework that integrates predictions from various models, enhancing overall accuracy by leveraging the individual strengths of each. This non-intrusive, model-agnostic approach treats the contributing models as black boxes and accommodates outputs from diverse methodologies, including machine learning algorithms and traditional numerical solvers. We advocate for a point-wise linear aggregation "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2409.17267","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-25T18:33:21Z","cross_cats_sorted":["cs.AI","cs.NA","math.NA","stat.ML"],"title_canon_sha256":"756ad9ecdf7f1c16b006716c5da2b3306031e82bd1f3c7df789ec4b88e6bdcc0","abstract_canon_sha256":"e6665f92f1b13cce9f692857ac9b4bbbb34d2d669d94c4f2f2cf84668e1dc851"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:22:50.588085Z","signature_b64":"f9MT6jArZvUYCA1n7aoM4BtqMghmk3ufjTPgixrITQrGGCPH9IHulT0x/VRqf7fg2VrEItnUsHRsRa9bhrVVDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c90f00b65d0e83678403a4fb30553793b47e1b1102828ee90792de1a5c16ca1d","last_reissued_at":"2026-07-05T10:22:50.587334Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:22:50.587334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Minimal Variance Model Aggregation: A principled, non-intrusive, and versatile integration of black box models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NA","math.NA","stat.ML"],"primary_cat":"cs.LG","authors_text":"Houman Owhadi, Th\\'eo Bourdais","submitted_at":"2024-09-25T18:33:21Z","abstract_excerpt":"Whether deterministic or stochastic, models can be viewed as functions designed to approximate a specific quantity of interest. We introduce Minimal Empirical Variance Aggregation (MEVA), a data-driven framework that integrates predictions from various models, enhancing overall accuracy by leveraging the individual strengths of each. This non-intrusive, model-agnostic approach treats the contributing models as black boxes and accommodates outputs from diverse methodologies, including machine learning algorithms and traditional numerical solvers. We advocate for a point-wise linear aggregation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17267","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.17267/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.17267","created_at":"2026-07-05T10:22:50.587438+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17267v2","created_at":"2026-07-05T10:22:50.587438+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17267","created_at":"2026-07-05T10:22:50.587438+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZEHQBNS5B2BW","created_at":"2026-07-05T10:22:50.587438+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZEHQBNS5B2BWPBAD","created_at":"2026-07-05T10:22:50.587438+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZEHQBNS5","created_at":"2026-07-05T10:22:50.587438+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.23859","citing_title":"HYCO: A Formalism for Hybrid-Cooperative PDE Modelling","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZEHQBNS5B2BWPBADUT5TAVJXSO","json":"https://pith.science/pith/ZEHQBNS5B2BWPBADUT5TAVJXSO.json","graph_json":"https://pith.science/api/pith-number/ZEHQBNS5B2BWPBADUT5TAVJXSO/graph.json","events_json":"https://pith.science/api/pith-number/ZEHQBNS5B2BWPBADUT5TAVJXSO/events.json","paper":"https://pith.science/paper/ZEHQBNS5"},"agent_actions":{"view_html":"https://pith.science/pith/ZEHQBNS5B2BWPBADUT5TAVJXSO","download_json":"https://pith.science/pith/ZEHQBNS5B2BWPBADUT5TAVJXSO.json","view_paper":"https://pith.science/paper/ZEHQBNS5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17267&json=true","fetch_graph":"https://pith.science/api/pith-number/ZEHQBNS5B2BWPBADUT5TAVJXSO/graph.json","fetch_events":"https://pith.science/api/pith-number/ZEHQBNS5B2BWPBADUT5TAVJXSO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZEHQBNS5B2BWPBADUT5TAVJXSO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZEHQBNS5B2BWPBADUT5TAVJXSO/action/storage_attestation","attest_author":"https://pith.science/pith/ZEHQBNS5B2BWPBADUT5TAVJXSO/action/author_attestation","sign_citation":"https://pith.science/pith/ZEHQBNS5B2BWPBADUT5TAVJXSO/action/citation_signature","submit_replication":"https://pith.science/pith/ZEHQBNS5B2BWPBADUT5TAVJXSO/action/replication_record"}},"created_at":"2026-07-05T10:22:50.587438+00:00","updated_at":"2026-07-05T10:22:50.587438+00:00"}