{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:NLAIBBH72RTBFK4FAEOTOAG7GE","short_pith_number":"pith:NLAIBBH7","schema_version":"1.0","canonical_sha256":"6ac08084ffd46612ab85011d3700df3116f4e0e6da2ab78a1c4230079d92b827","source":{"kind":"arxiv","id":"1602.04889","version":3},"attestation_state":"computed","paper":{"title":"Unsupervised Domain Adaptation Using Approximate Label Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Barbara E. Engelhardt, Jordan T. Ash, Robert E. Schapire","submitted_at":"2016-02-16T02:38:25Z","abstract_excerpt":"Domain adaptation addresses the problem created when training data is generated by a so-called source distribution, but test data is generated by a significantly different target distribution. In this work, we present approximate label matching (ALM), a new unsupervised domain adaptation technique that creates and leverages a rough labeling on the test samples, then uses these noisy labels to learn a transformation that aligns the source and target samples. We show that the transformation estimated by ALM has favorable properties compared to transformations estimated by other methods, which do"},"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":"1602.04889","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-02-16T02:38:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cc82e807af0e1b2912ab3516f78029a2ef28e685b7c757dc084407ef69df1d2f","abstract_canon_sha256":"a9dbd80c57dcea3c922214929115485b0f2a2c7eeed9b0095a02569316678c66"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:42.541209Z","signature_b64":"xaVJJhbwPGlLF/wlcYhzr4qJdXkUVhDWvRwWQrCEITHLs/IolWqltIMdKuIgNUTmETlH0Ub7tqrKmi9n0FHDDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ac08084ffd46612ab85011d3700df3116f4e0e6da2ab78a1c4230079d92b827","last_reissued_at":"2026-05-18T00:49:42.540698Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:42.540698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Domain Adaptation Using Approximate Label Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Barbara E. Engelhardt, Jordan T. Ash, Robert E. Schapire","submitted_at":"2016-02-16T02:38:25Z","abstract_excerpt":"Domain adaptation addresses the problem created when training data is generated by a so-called source distribution, but test data is generated by a significantly different target distribution. In this work, we present approximate label matching (ALM), a new unsupervised domain adaptation technique that creates and leverages a rough labeling on the test samples, then uses these noisy labels to learn a transformation that aligns the source and target samples. We show that the transformation estimated by ALM has favorable properties compared to transformations estimated by other methods, which do"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.04889","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1602.04889","created_at":"2026-05-18T00:49:42.540771+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.04889v3","created_at":"2026-05-18T00:49:42.540771+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.04889","created_at":"2026-05-18T00:49:42.540771+00:00"},{"alias_kind":"pith_short_12","alias_value":"NLAIBBH72RTB","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"NLAIBBH72RTBFK4F","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"NLAIBBH7","created_at":"2026-05-18T12:30:32.724797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NLAIBBH72RTBFK4FAEOTOAG7GE","json":"https://pith.science/pith/NLAIBBH72RTBFK4FAEOTOAG7GE.json","graph_json":"https://pith.science/api/pith-number/NLAIBBH72RTBFK4FAEOTOAG7GE/graph.json","events_json":"https://pith.science/api/pith-number/NLAIBBH72RTBFK4FAEOTOAG7GE/events.json","paper":"https://pith.science/paper/NLAIBBH7"},"agent_actions":{"view_html":"https://pith.science/pith/NLAIBBH72RTBFK4FAEOTOAG7GE","download_json":"https://pith.science/pith/NLAIBBH72RTBFK4FAEOTOAG7GE.json","view_paper":"https://pith.science/paper/NLAIBBH7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.04889&json=true","fetch_graph":"https://pith.science/api/pith-number/NLAIBBH72RTBFK4FAEOTOAG7GE/graph.json","fetch_events":"https://pith.science/api/pith-number/NLAIBBH72RTBFK4FAEOTOAG7GE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NLAIBBH72RTBFK4FAEOTOAG7GE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NLAIBBH72RTBFK4FAEOTOAG7GE/action/storage_attestation","attest_author":"https://pith.science/pith/NLAIBBH72RTBFK4FAEOTOAG7GE/action/author_attestation","sign_citation":"https://pith.science/pith/NLAIBBH72RTBFK4FAEOTOAG7GE/action/citation_signature","submit_replication":"https://pith.science/pith/NLAIBBH72RTBFK4FAEOTOAG7GE/action/replication_record"}},"created_at":"2026-05-18T00:49:42.540771+00:00","updated_at":"2026-05-18T00:49:42.540771+00:00"}