{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:N2MXQSAI22CNPYY6RSKSJMW4DQ","short_pith_number":"pith:N2MXQSAI","schema_version":"1.0","canonical_sha256":"6e99784808d684d7e31e8c9524b2dc1c15427ef83738a6821c698b5c31c659fb","source":{"kind":"arxiv","id":"2402.01401","version":4},"attestation_state":"computed","paper":{"title":"An Information Theoretic Approach to Machine Unlearning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandra Brintrup, Cengiz \\\"Oztireli, Jack Foster, Kyle Fogarty, Stefan Schoepf, Zack Dugue","submitted_at":"2024-02-02T13:33:30Z","abstract_excerpt":"To comply with AI and data regulations, the need to forget private or copyrighted information from trained machine learning models is increasingly important. The key challenge in unlearning is forgetting the necessary data in a timely manner, while preserving model performance. In this work, we address the zero-shot unlearning scenario, whereby an unlearning algorithm must be able to remove data given only a trained model and the data to be forgotten. We explore unlearning from an information theoretic perspective, connecting the influence of a sample to the information gain a model receives b"},"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":"2402.01401","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-02T13:33:30Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"32b8014cb276437e1026824e17135bcc36b2d1a1bc4b51694b4cc08eb57ab7dd","abstract_canon_sha256":"185c3260ba061b239de7f640ad59a0281ed6fe096990e0066c0be2c153eb8173"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:22.514079Z","signature_b64":"TPrgw0clFx0CUJVaX8KiUs4WrX8jGUXATynAgc9CTyY/K2xxihPMc+OGm0iJYzc9gjlX9ITJp3zujyP5rqF2Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e99784808d684d7e31e8c9524b2dc1c15427ef83738a6821c698b5c31c659fb","last_reissued_at":"2026-07-05T09:42:22.513550Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:22.513550Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Information Theoretic Approach to Machine Unlearning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexandra Brintrup, Cengiz \\\"Oztireli, Jack Foster, Kyle Fogarty, Stefan Schoepf, Zack Dugue","submitted_at":"2024-02-02T13:33:30Z","abstract_excerpt":"To comply with AI and data regulations, the need to forget private or copyrighted information from trained machine learning models is increasingly important. The key challenge in unlearning is forgetting the necessary data in a timely manner, while preserving model performance. In this work, we address the zero-shot unlearning scenario, whereby an unlearning algorithm must be able to remove data given only a trained model and the data to be forgotten. We explore unlearning from an information theoretic perspective, connecting the influence of a sample to the information gain a model receives b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.01401","kind":"arxiv","version":4},"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/2402.01401/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":"2402.01401","created_at":"2026-07-05T09:42:22.513608+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.01401v4","created_at":"2026-07-05T09:42:22.513608+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.01401","created_at":"2026-07-05T09:42:22.513608+00:00"},{"alias_kind":"pith_short_12","alias_value":"N2MXQSAI22CN","created_at":"2026-07-05T09:42:22.513608+00:00"},{"alias_kind":"pith_short_16","alias_value":"N2MXQSAI22CNPYY6","created_at":"2026-07-05T09:42:22.513608+00:00"},{"alias_kind":"pith_short_8","alias_value":"N2MXQSAI","created_at":"2026-07-05T09:42:22.513608+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.18610","citing_title":"CATA: Continual Machine Unlearning via Conflict-Averse Task Arithmetic","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14309","citing_title":"ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14309","citing_title":"ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04030","citing_title":"Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08238","citing_title":"$\\oslash$ Source Models Leak What They Shouldn't $\\nrightarrow$: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N2MXQSAI22CNPYY6RSKSJMW4DQ","json":"https://pith.science/pith/N2MXQSAI22CNPYY6RSKSJMW4DQ.json","graph_json":"https://pith.science/api/pith-number/N2MXQSAI22CNPYY6RSKSJMW4DQ/graph.json","events_json":"https://pith.science/api/pith-number/N2MXQSAI22CNPYY6RSKSJMW4DQ/events.json","paper":"https://pith.science/paper/N2MXQSAI"},"agent_actions":{"view_html":"https://pith.science/pith/N2MXQSAI22CNPYY6RSKSJMW4DQ","download_json":"https://pith.science/pith/N2MXQSAI22CNPYY6RSKSJMW4DQ.json","view_paper":"https://pith.science/paper/N2MXQSAI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.01401&json=true","fetch_graph":"https://pith.science/api/pith-number/N2MXQSAI22CNPYY6RSKSJMW4DQ/graph.json","fetch_events":"https://pith.science/api/pith-number/N2MXQSAI22CNPYY6RSKSJMW4DQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N2MXQSAI22CNPYY6RSKSJMW4DQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N2MXQSAI22CNPYY6RSKSJMW4DQ/action/storage_attestation","attest_author":"https://pith.science/pith/N2MXQSAI22CNPYY6RSKSJMW4DQ/action/author_attestation","sign_citation":"https://pith.science/pith/N2MXQSAI22CNPYY6RSKSJMW4DQ/action/citation_signature","submit_replication":"https://pith.science/pith/N2MXQSAI22CNPYY6RSKSJMW4DQ/action/replication_record"}},"created_at":"2026-07-05T09:42:22.513608+00:00","updated_at":"2026-07-05T09:42:22.513608+00:00"}