{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:KGTJUEQCMFE6POTLJDD5LR55ME","short_pith_number":"pith:KGTJUEQC","schema_version":"1.0","canonical_sha256":"51a69a12026149e7ba6b48c7d5c7bd61081ccc68ed38d153579bfd465abdcc10","source":{"kind":"arxiv","id":"2110.15057","version":2},"attestation_state":"computed","paper":{"title":"Mapping conditional distributions for domain adaptation under generalized target shift","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alain Rakotomamonjy (LITIS), Emmanuel de Bezenac (MLIA), Matthieu Kirchmeyer (MLIA), Patrick Gallinari (MLIA)","submitted_at":"2021-10-26T14:25:07Z","abstract_excerpt":"We consider the problem of unsupervised domain adaptation (UDA) between a source and a target domain under conditional and label shift a.k.a Generalized Target Shift (GeTarS). Unlike simpler UDA settings, few works have addressed this challenging problem. Recent approaches learn domain-invariant representations, yet they have practical limitations and rely on strong assumptions that may not hold in practice. In this paper, we explore a novel and general approach to align pretrained representations, which circumvents existing drawbacks. Instead of constraining representation invariance, it lear"},"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":"2110.15057","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-26T14:25:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"95bcb0261e63e7d8c7db47ac9f5240a333a805f6a2d0780e584e24911f7a19c5","abstract_canon_sha256":"fa72499c90a6671a30fd4b0c54862ff92935b4fb5a65e779a288b8f40a10c8ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:06:17.822250Z","signature_b64":"8rYAQbUQXDHU+e37g7bC99Th8UkkU0ePay60ZDWDWElHVbXLuerjNoG7Q6yMcIZGmkyzTK/iUuegEg2I9RQFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51a69a12026149e7ba6b48c7d5c7bd61081ccc68ed38d153579bfd465abdcc10","last_reissued_at":"2026-07-05T04:06:17.821150Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:06:17.821150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mapping conditional distributions for domain adaptation under generalized target shift","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Alain Rakotomamonjy (LITIS), Emmanuel de Bezenac (MLIA), Matthieu Kirchmeyer (MLIA), Patrick Gallinari (MLIA)","submitted_at":"2021-10-26T14:25:07Z","abstract_excerpt":"We consider the problem of unsupervised domain adaptation (UDA) between a source and a target domain under conditional and label shift a.k.a Generalized Target Shift (GeTarS). Unlike simpler UDA settings, few works have addressed this challenging problem. Recent approaches learn domain-invariant representations, yet they have practical limitations and rely on strong assumptions that may not hold in practice. In this paper, we explore a novel and general approach to align pretrained representations, which circumvents existing drawbacks. Instead of constraining representation invariance, it lear"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.15057","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/2110.15057/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":"2110.15057","created_at":"2026-07-05T04:06:17.821243+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.15057v2","created_at":"2026-07-05T04:06:17.821243+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.15057","created_at":"2026-07-05T04:06:17.821243+00:00"},{"alias_kind":"pith_short_12","alias_value":"KGTJUEQCMFE6","created_at":"2026-07-05T04:06:17.821243+00:00"},{"alias_kind":"pith_short_16","alias_value":"KGTJUEQCMFE6POTL","created_at":"2026-07-05T04:06:17.821243+00:00"},{"alias_kind":"pith_short_8","alias_value":"KGTJUEQC","created_at":"2026-07-05T04:06:17.821243+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/KGTJUEQCMFE6POTLJDD5LR55ME","json":"https://pith.science/pith/KGTJUEQCMFE6POTLJDD5LR55ME.json","graph_json":"https://pith.science/api/pith-number/KGTJUEQCMFE6POTLJDD5LR55ME/graph.json","events_json":"https://pith.science/api/pith-number/KGTJUEQCMFE6POTLJDD5LR55ME/events.json","paper":"https://pith.science/paper/KGTJUEQC"},"agent_actions":{"view_html":"https://pith.science/pith/KGTJUEQCMFE6POTLJDD5LR55ME","download_json":"https://pith.science/pith/KGTJUEQCMFE6POTLJDD5LR55ME.json","view_paper":"https://pith.science/paper/KGTJUEQC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.15057&json=true","fetch_graph":"https://pith.science/api/pith-number/KGTJUEQCMFE6POTLJDD5LR55ME/graph.json","fetch_events":"https://pith.science/api/pith-number/KGTJUEQCMFE6POTLJDD5LR55ME/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KGTJUEQCMFE6POTLJDD5LR55ME/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KGTJUEQCMFE6POTLJDD5LR55ME/action/storage_attestation","attest_author":"https://pith.science/pith/KGTJUEQCMFE6POTLJDD5LR55ME/action/author_attestation","sign_citation":"https://pith.science/pith/KGTJUEQCMFE6POTLJDD5LR55ME/action/citation_signature","submit_replication":"https://pith.science/pith/KGTJUEQCMFE6POTLJDD5LR55ME/action/replication_record"}},"created_at":"2026-07-05T04:06:17.821243+00:00","updated_at":"2026-07-05T04:06:17.821243+00:00"}