{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:2CET2ZR3HQLXYL2YN2XYVURNX3","short_pith_number":"pith:2CET2ZR3","schema_version":"1.0","canonical_sha256":"d0893d663b3c177c2f586eaf8ad22dbecd2c2f15cc31e42efee0923f7e444eb5","source":{"kind":"arxiv","id":"2004.11829","version":6},"attestation_state":"computed","paper":{"title":"A survey on domain adaptation theory: learning bounds and theoretical guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Amaury Habrard, Emilie Morvant, Ievgen Redko, Marc Sebban, Youn\\`es Bennani","submitted_at":"2020-04-24T16:11:03Z","abstract_excerpt":"All famous machine learning algorithms that comprise both supervised and semi-supervised learning work well only under a common assumption: the training and test data follow the same distribution. When the distribution changes, most statistical models must be reconstructed from newly collected data, which for some applications can be costly or impossible to obtain. Therefore, it has become necessary to develop approaches that reduce the need and the effort to obtain new labeled samples by exploiting data that are available in related areas, and using these further across similar fields. This h"},"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":"2004.11829","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-04-24T16:11:03Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"dcee9e35721dbfd4101f799e6fde10037c2f7e043f4a89b91f0748cc0471ec1d","abstract_canon_sha256":"8fa5bf4f1c12f40d4dfd2270db9671a32bbe9c763452144e98cdd33360a7cf29"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:40:05.817741Z","signature_b64":"Idpl55NJ6l2KfTXNj484o+T5Gs4nMpgjBsRfvzZlZzs6S/SCet4/Go94RxXmUaD9epdHnpvn80g3VRc/J1UwCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0893d663b3c177c2f586eaf8ad22dbecd2c2f15cc31e42efee0923f7e444eb5","last_reissued_at":"2026-07-05T04:40:05.817346Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:40:05.817346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A survey on domain adaptation theory: learning bounds and theoretical guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Amaury Habrard, Emilie Morvant, Ievgen Redko, Marc Sebban, Youn\\`es Bennani","submitted_at":"2020-04-24T16:11:03Z","abstract_excerpt":"All famous machine learning algorithms that comprise both supervised and semi-supervised learning work well only under a common assumption: the training and test data follow the same distribution. When the distribution changes, most statistical models must be reconstructed from newly collected data, which for some applications can be costly or impossible to obtain. Therefore, it has become necessary to develop approaches that reduce the need and the effort to obtain new labeled samples by exploiting data that are available in related areas, and using these further across similar fields. This h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.11829","kind":"arxiv","version":6},"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/2004.11829/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":"2004.11829","created_at":"2026-07-05T04:40:05.817403+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.11829v6","created_at":"2026-07-05T04:40:05.817403+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.11829","created_at":"2026-07-05T04:40:05.817403+00:00"},{"alias_kind":"pith_short_12","alias_value":"2CET2ZR3HQLX","created_at":"2026-07-05T04:40:05.817403+00:00"},{"alias_kind":"pith_short_16","alias_value":"2CET2ZR3HQLXYL2Y","created_at":"2026-07-05T04:40:05.817403+00:00"},{"alias_kind":"pith_short_8","alias_value":"2CET2ZR3","created_at":"2026-07-05T04:40:05.817403+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12680","citing_title":"How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2307.08643","citing_title":"Corruptions of Supervised Learning Problems: Typology and Mitigations","ref_index":109,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07005","citing_title":"Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift","ref_index":258,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07005","citing_title":"Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift","ref_index":258,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2CET2ZR3HQLXYL2YN2XYVURNX3","json":"https://pith.science/pith/2CET2ZR3HQLXYL2YN2XYVURNX3.json","graph_json":"https://pith.science/api/pith-number/2CET2ZR3HQLXYL2YN2XYVURNX3/graph.json","events_json":"https://pith.science/api/pith-number/2CET2ZR3HQLXYL2YN2XYVURNX3/events.json","paper":"https://pith.science/paper/2CET2ZR3"},"agent_actions":{"view_html":"https://pith.science/pith/2CET2ZR3HQLXYL2YN2XYVURNX3","download_json":"https://pith.science/pith/2CET2ZR3HQLXYL2YN2XYVURNX3.json","view_paper":"https://pith.science/paper/2CET2ZR3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.11829&json=true","fetch_graph":"https://pith.science/api/pith-number/2CET2ZR3HQLXYL2YN2XYVURNX3/graph.json","fetch_events":"https://pith.science/api/pith-number/2CET2ZR3HQLXYL2YN2XYVURNX3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2CET2ZR3HQLXYL2YN2XYVURNX3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2CET2ZR3HQLXYL2YN2XYVURNX3/action/storage_attestation","attest_author":"https://pith.science/pith/2CET2ZR3HQLXYL2YN2XYVURNX3/action/author_attestation","sign_citation":"https://pith.science/pith/2CET2ZR3HQLXYL2YN2XYVURNX3/action/citation_signature","submit_replication":"https://pith.science/pith/2CET2ZR3HQLXYL2YN2XYVURNX3/action/replication_record"}},"created_at":"2026-07-05T04:40:05.817403+00:00","updated_at":"2026-07-05T04:40:05.817403+00:00"}