{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AC2A2IGNHDGTULEIQIHCEOAC3T","short_pith_number":"pith:AC2A2IGN","schema_version":"1.0","canonical_sha256":"00b40d20cd38cd3a2c88820e223802dcd2806ec77425c9af9eac5ac50cfbe127","source":{"kind":"arxiv","id":"2508.21181","version":1},"attestation_state":"computed","paper":{"title":"FUTURE: Flexible Unlearning for Tree Ensemble","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hadi Amiri, Jiali Cheng, Jin Huang, Kaushiki Nag, Lalitesh Morishetti, Mengjie Wang, Yuchan Guo, Ziheng Chen","submitted_at":"2025-08-28T19:45:36Z","abstract_excerpt":"Tree ensembles are widely recognized for their effectiveness in classification tasks, achieving state-of-the-art performance across diverse domains, including bioinformatics, finance, and medical diagnosis. With increasing emphasis on data privacy and the \\textit{right to be forgotten}, several unlearning algorithms have been proposed to enable tree ensembles to forget sensitive information. However, existing methods are often tailored to a particular model or rely on the discrete tree structure, making them difficult to generalize to complex ensembles and inefficient for large-scale datasets."},"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":"2508.21181","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-28T19:45:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5994f86f7e804cc7eeda6d065d602e362bf239ae8d2df642ea0affbf9ccd177e","abstract_canon_sha256":"87a8471e267aac5148081a07e2b8ebf975a7cdf76639cff39679e502d0988ded"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:01:29.568363Z","signature_b64":"8jA2p4rLUGYAnHUP8L64Mc8G+dcENbAsMc3bXQCj+6zFRO83VE2dS08Q9M/HoVq9ZcebqpZGiy3zie++JXjdBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00b40d20cd38cd3a2c88820e223802dcd2806ec77425c9af9eac5ac50cfbe127","last_reissued_at":"2026-07-05T12:01:29.567839Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:01:29.567839Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FUTURE: Flexible Unlearning for Tree Ensemble","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hadi Amiri, Jiali Cheng, Jin Huang, Kaushiki Nag, Lalitesh Morishetti, Mengjie Wang, Yuchan Guo, Ziheng Chen","submitted_at":"2025-08-28T19:45:36Z","abstract_excerpt":"Tree ensembles are widely recognized for their effectiveness in classification tasks, achieving state-of-the-art performance across diverse domains, including bioinformatics, finance, and medical diagnosis. With increasing emphasis on data privacy and the \\textit{right to be forgotten}, several unlearning algorithms have been proposed to enable tree ensembles to forget sensitive information. However, existing methods are often tailored to a particular model or rely on the discrete tree structure, making them difficult to generalize to complex ensembles and inefficient for large-scale datasets."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.21181","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/2508.21181/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":"2508.21181","created_at":"2026-07-05T12:01:29.567901+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.21181v1","created_at":"2026-07-05T12:01:29.567901+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.21181","created_at":"2026-07-05T12:01:29.567901+00:00"},{"alias_kind":"pith_short_12","alias_value":"AC2A2IGNHDGT","created_at":"2026-07-05T12:01:29.567901+00:00"},{"alias_kind":"pith_short_16","alias_value":"AC2A2IGNHDGTULEI","created_at":"2026-07-05T12:01:29.567901+00:00"},{"alias_kind":"pith_short_8","alias_value":"AC2A2IGN","created_at":"2026-07-05T12:01:29.567901+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/AC2A2IGNHDGTULEIQIHCEOAC3T","json":"https://pith.science/pith/AC2A2IGNHDGTULEIQIHCEOAC3T.json","graph_json":"https://pith.science/api/pith-number/AC2A2IGNHDGTULEIQIHCEOAC3T/graph.json","events_json":"https://pith.science/api/pith-number/AC2A2IGNHDGTULEIQIHCEOAC3T/events.json","paper":"https://pith.science/paper/AC2A2IGN"},"agent_actions":{"view_html":"https://pith.science/pith/AC2A2IGNHDGTULEIQIHCEOAC3T","download_json":"https://pith.science/pith/AC2A2IGNHDGTULEIQIHCEOAC3T.json","view_paper":"https://pith.science/paper/AC2A2IGN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.21181&json=true","fetch_graph":"https://pith.science/api/pith-number/AC2A2IGNHDGTULEIQIHCEOAC3T/graph.json","fetch_events":"https://pith.science/api/pith-number/AC2A2IGNHDGTULEIQIHCEOAC3T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AC2A2IGNHDGTULEIQIHCEOAC3T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AC2A2IGNHDGTULEIQIHCEOAC3T/action/storage_attestation","attest_author":"https://pith.science/pith/AC2A2IGNHDGTULEIQIHCEOAC3T/action/author_attestation","sign_citation":"https://pith.science/pith/AC2A2IGNHDGTULEIQIHCEOAC3T/action/citation_signature","submit_replication":"https://pith.science/pith/AC2A2IGNHDGTULEIQIHCEOAC3T/action/replication_record"}},"created_at":"2026-07-05T12:01:29.567901+00:00","updated_at":"2026-07-05T12:01:29.567901+00:00"}