{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:W6XJJ2CAP2UKWO2AVL52BJVH5X","short_pith_number":"pith:W6XJJ2CA","schema_version":"1.0","canonical_sha256":"b7ae94e8407ea8ab3b40aafba0a6a7edd5f516d5ab2dc0d1c6fdaa92217678fe","source":{"kind":"arxiv","id":"2103.03279","version":2},"attestation_state":"computed","paper":{"title":"Remember What You Want to Forget: Algorithms for Machine Unlearning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ananda Theertha Suresh, Ayush Sekhari, Gautam Kamath, Jayadev Acharya","submitted_at":"2021-03-04T19:28:57Z","abstract_excerpt":"We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset $S$ drawn i.i.d. from an unknown distribution, and outputs a model $\\widehat{w}$ that performs well on unseen samples from the same distribution. However, at some point in the future, any training datapoint $z \\in S$ can request to be unlearned, thus prompting the learner to modify its output model while still ensuring the same accuracy guarantees. We initiate a rigorous study of generalization in machine unlearning, where the goal is to perform well on previously unseen datapoints. Our focu"},"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":"2103.03279","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-04T19:28:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6c6ae20a42573145d88af869bafe8c27cac6f817a2d615036456c6b299499630","abstract_canon_sha256":"513518497cac07725b2003cb99771050d04490d684ef69514c4352a590da9309"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:59:55.789420Z","signature_b64":"qCLZue2slvccQOxplch6a+FvD3GqOt07ua9lHH7P02afs2W0gMfOpht3bVt+dDx1qgNVAQ2NDNvaAl5MuiTCAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7ae94e8407ea8ab3b40aafba0a6a7edd5f516d5ab2dc0d1c6fdaa92217678fe","last_reissued_at":"2026-07-05T02:59:55.788984Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:59:55.788984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Remember What You Want to Forget: Algorithms for Machine Unlearning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ananda Theertha Suresh, Ayush Sekhari, Gautam Kamath, Jayadev Acharya","submitted_at":"2021-03-04T19:28:57Z","abstract_excerpt":"We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset $S$ drawn i.i.d. from an unknown distribution, and outputs a model $\\widehat{w}$ that performs well on unseen samples from the same distribution. However, at some point in the future, any training datapoint $z \\in S$ can request to be unlearned, thus prompting the learner to modify its output model while still ensuring the same accuracy guarantees. We initiate a rigorous study of generalization in machine unlearning, where the goal is to perform well on previously unseen datapoints. Our focu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.03279","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/2103.03279/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":"2103.03279","created_at":"2026-07-05T02:59:55.789046+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.03279v2","created_at":"2026-07-05T02:59:55.789046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.03279","created_at":"2026-07-05T02:59:55.789046+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6XJJ2CAP2UK","created_at":"2026-07-05T02:59:55.789046+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6XJJ2CAP2UKWO2A","created_at":"2026-07-05T02:59:55.789046+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6XJJ2CA","created_at":"2026-07-05T02:59:55.789046+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17590","citing_title":"Form and Function: Machine Unlearning as a Problem of Misaligned States","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11170","citing_title":"Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data","ref_index":163,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23046","citing_title":"Shape of Memory: a Geometric Analysis of Machine Unlearning in Second-Order Optimizers","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W6XJJ2CAP2UKWO2AVL52BJVH5X","json":"https://pith.science/pith/W6XJJ2CAP2UKWO2AVL52BJVH5X.json","graph_json":"https://pith.science/api/pith-number/W6XJJ2CAP2UKWO2AVL52BJVH5X/graph.json","events_json":"https://pith.science/api/pith-number/W6XJJ2CAP2UKWO2AVL52BJVH5X/events.json","paper":"https://pith.science/paper/W6XJJ2CA"},"agent_actions":{"view_html":"https://pith.science/pith/W6XJJ2CAP2UKWO2AVL52BJVH5X","download_json":"https://pith.science/pith/W6XJJ2CAP2UKWO2AVL52BJVH5X.json","view_paper":"https://pith.science/paper/W6XJJ2CA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.03279&json=true","fetch_graph":"https://pith.science/api/pith-number/W6XJJ2CAP2UKWO2AVL52BJVH5X/graph.json","fetch_events":"https://pith.science/api/pith-number/W6XJJ2CAP2UKWO2AVL52BJVH5X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6XJJ2CAP2UKWO2AVL52BJVH5X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6XJJ2CAP2UKWO2AVL52BJVH5X/action/storage_attestation","attest_author":"https://pith.science/pith/W6XJJ2CAP2UKWO2AVL52BJVH5X/action/author_attestation","sign_citation":"https://pith.science/pith/W6XJJ2CAP2UKWO2AVL52BJVH5X/action/citation_signature","submit_replication":"https://pith.science/pith/W6XJJ2CAP2UKWO2AVL52BJVH5X/action/replication_record"}},"created_at":"2026-07-05T02:59:55.789046+00:00","updated_at":"2026-07-05T02:59:55.789046+00:00"}