{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Y35D3SEXJRGL4HUUFWPAUJEX26","short_pith_number":"pith:Y35D3SEX","schema_version":"1.0","canonical_sha256":"c6fa3dc8974c4cbe1e942d9e0a2497d79437e9ec28eee4d24e2e3ccaa0b429f5","source":{"kind":"arxiv","id":"2509.08176","version":1},"attestation_state":"computed","paper":{"title":"MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Honghui Du, Huiyu Zhou, Leandro Minku","submitted_at":"2025-09-09T22:51:31Z","abstract_excerpt":"Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data streams from different sources as a strategy to tackle concept drift in a given target domain. These approaches make the assumption that at least one of the source models represents a concept similar to the target concept, which may not hold in many real-world scenarios. In this paper, we propose a novel approach called Multi-source mApping with tRansfer LearnIng for Non-stationary Environments (MARLINE). MARLINE can benef"},"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":"2509.08176","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-09-09T22:51:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e9850dff51d1bbe1f66d5ba66ac29ac5717dc5187b78c06a56c87a8770faebcd","abstract_canon_sha256":"1b38f69dda648501784ff2c1b1d045da51ab74c5bb36efffa411eb095c35e54c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:08:24.414116Z","signature_b64":"5x1hdbF1Kx9c5bsPoEU2ZvXe67l9xQy3wMaM3YRqQko+rnYyh2BwrY6x4OgZwog4UEViV1p8aN9ObDZJVMY7Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6fa3dc8974c4cbe1e942d9e0a2497d79437e9ec28eee4d24e2e3ccaa0b429f5","last_reissued_at":"2026-07-05T12:08:24.413571Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:08:24.413571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Honghui Du, Huiyu Zhou, Leandro Minku","submitted_at":"2025-09-09T22:51:31Z","abstract_excerpt":"Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data streams from different sources as a strategy to tackle concept drift in a given target domain. These approaches make the assumption that at least one of the source models represents a concept similar to the target concept, which may not hold in many real-world scenarios. In this paper, we propose a novel approach called Multi-source mApping with tRansfer LearnIng for Non-stationary Environments (MARLINE). MARLINE can benef"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.08176","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/2509.08176/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":"2509.08176","created_at":"2026-07-05T12:08:24.413632+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.08176v1","created_at":"2026-07-05T12:08:24.413632+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.08176","created_at":"2026-07-05T12:08:24.413632+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y35D3SEXJRGL","created_at":"2026-07-05T12:08:24.413632+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y35D3SEXJRGL4HUU","created_at":"2026-07-05T12:08:24.413632+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y35D3SEX","created_at":"2026-07-05T12:08:24.413632+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/Y35D3SEXJRGL4HUUFWPAUJEX26","json":"https://pith.science/pith/Y35D3SEXJRGL4HUUFWPAUJEX26.json","graph_json":"https://pith.science/api/pith-number/Y35D3SEXJRGL4HUUFWPAUJEX26/graph.json","events_json":"https://pith.science/api/pith-number/Y35D3SEXJRGL4HUUFWPAUJEX26/events.json","paper":"https://pith.science/paper/Y35D3SEX"},"agent_actions":{"view_html":"https://pith.science/pith/Y35D3SEXJRGL4HUUFWPAUJEX26","download_json":"https://pith.science/pith/Y35D3SEXJRGL4HUUFWPAUJEX26.json","view_paper":"https://pith.science/paper/Y35D3SEX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.08176&json=true","fetch_graph":"https://pith.science/api/pith-number/Y35D3SEXJRGL4HUUFWPAUJEX26/graph.json","fetch_events":"https://pith.science/api/pith-number/Y35D3SEXJRGL4HUUFWPAUJEX26/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y35D3SEXJRGL4HUUFWPAUJEX26/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y35D3SEXJRGL4HUUFWPAUJEX26/action/storage_attestation","attest_author":"https://pith.science/pith/Y35D3SEXJRGL4HUUFWPAUJEX26/action/author_attestation","sign_citation":"https://pith.science/pith/Y35D3SEXJRGL4HUUFWPAUJEX26/action/citation_signature","submit_replication":"https://pith.science/pith/Y35D3SEXJRGL4HUUFWPAUJEX26/action/replication_record"}},"created_at":"2026-07-05T12:08:24.413632+00:00","updated_at":"2026-07-05T12:08:24.413632+00:00"}