{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SDX5E6P3MAZTIRSCZX35IPOLP3","short_pith_number":"pith:SDX5E6P3","schema_version":"1.0","canonical_sha256":"90efd279fb6033344642cdf7d43dcb7ed114598d94a724c48761f7509958fdcb","source":{"kind":"arxiv","id":"2401.16410","version":1},"attestation_state":"computed","paper":{"title":"ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hwanwoo Kim, Jiwei Zhao, Qinglong Tian, Xin Zhang","submitted_at":"2024-01-29T18:47:36Z","abstract_excerpt":"The presence of distribution shifts poses a significant challenge for deploying modern machine learning models in real-world applications. This work focuses on the target shift problem in a regression setting (Zhang et al., 2013; Nguyen et al., 2016). More specifically, the target variable y (also known as the response variable), which is continuous, has different marginal distributions in the training source and testing domain, while the conditional distribution of features x given y remains the same. While most literature focuses on classification tasks with finite target space, the regressi"},"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":"2401.16410","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-01-29T18:47:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a0e1bdebbe1ce29c40b279872ea32f7af7fabba39a542beadf7a209efaa57cb3","abstract_canon_sha256":"9738defe50c643143608103ddf8256bc74bcb42bf469a34115079b18ab63316d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:38:48.750290Z","signature_b64":"R3RRB4e5ee0gW5n/7HbmOtA1p1oVUz6jxk/ld7zAH4ixTORd2mzq1mQ+rW/kmdopaAynBM0lrn540IpPk9+xAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90efd279fb6033344642cdf7d43dcb7ed114598d94a724c48761f7509958fdcb","last_reissued_at":"2026-07-05T07:38:48.749877Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:38:48.749877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hwanwoo Kim, Jiwei Zhao, Qinglong Tian, Xin Zhang","submitted_at":"2024-01-29T18:47:36Z","abstract_excerpt":"The presence of distribution shifts poses a significant challenge for deploying modern machine learning models in real-world applications. This work focuses on the target shift problem in a regression setting (Zhang et al., 2013; Nguyen et al., 2016). More specifically, the target variable y (also known as the response variable), which is continuous, has different marginal distributions in the training source and testing domain, while the conditional distribution of features x given y remains the same. While most literature focuses on classification tasks with finite target space, the regressi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.16410","kind":"arxiv","version":1},"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/2401.16410/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":"2401.16410","created_at":"2026-07-05T07:38:48.749945+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.16410v1","created_at":"2026-07-05T07:38:48.749945+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.16410","created_at":"2026-07-05T07:38:48.749945+00:00"},{"alias_kind":"pith_short_12","alias_value":"SDX5E6P3MAZT","created_at":"2026-07-05T07:38:48.749945+00:00"},{"alias_kind":"pith_short_16","alias_value":"SDX5E6P3MAZTIRSC","created_at":"2026-07-05T07:38:48.749945+00:00"},{"alias_kind":"pith_short_8","alias_value":"SDX5E6P3","created_at":"2026-07-05T07:38:48.749945+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/SDX5E6P3MAZTIRSCZX35IPOLP3","json":"https://pith.science/pith/SDX5E6P3MAZTIRSCZX35IPOLP3.json","graph_json":"https://pith.science/api/pith-number/SDX5E6P3MAZTIRSCZX35IPOLP3/graph.json","events_json":"https://pith.science/api/pith-number/SDX5E6P3MAZTIRSCZX35IPOLP3/events.json","paper":"https://pith.science/paper/SDX5E6P3"},"agent_actions":{"view_html":"https://pith.science/pith/SDX5E6P3MAZTIRSCZX35IPOLP3","download_json":"https://pith.science/pith/SDX5E6P3MAZTIRSCZX35IPOLP3.json","view_paper":"https://pith.science/paper/SDX5E6P3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.16410&json=true","fetch_graph":"https://pith.science/api/pith-number/SDX5E6P3MAZTIRSCZX35IPOLP3/graph.json","fetch_events":"https://pith.science/api/pith-number/SDX5E6P3MAZTIRSCZX35IPOLP3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SDX5E6P3MAZTIRSCZX35IPOLP3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SDX5E6P3MAZTIRSCZX35IPOLP3/action/storage_attestation","attest_author":"https://pith.science/pith/SDX5E6P3MAZTIRSCZX35IPOLP3/action/author_attestation","sign_citation":"https://pith.science/pith/SDX5E6P3MAZTIRSCZX35IPOLP3/action/citation_signature","submit_replication":"https://pith.science/pith/SDX5E6P3MAZTIRSCZX35IPOLP3/action/replication_record"}},"created_at":"2026-07-05T07:38:48.749945+00:00","updated_at":"2026-07-05T07:38:48.749945+00:00"}