{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:N2MXQSAI22CNPYY6RSKSJMW4DQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"185c3260ba061b239de7f640ad59a0281ed6fe096990e0066c0be2c153eb8173","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-02T13:33:30Z","title_canon_sha256":"32b8014cb276437e1026824e17135bcc36b2d1a1bc4b51694b4cc08eb57ab7dd"},"schema_version":"1.0","source":{"id":"2402.01401","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.01401","created_at":"2026-07-05T09:42:22Z"},{"alias_kind":"arxiv_version","alias_value":"2402.01401v4","created_at":"2026-07-05T09:42:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.01401","created_at":"2026-07-05T09:42:22Z"},{"alias_kind":"pith_short_12","alias_value":"N2MXQSAI22CN","created_at":"2026-07-05T09:42:22Z"},{"alias_kind":"pith_short_16","alias_value":"N2MXQSAI22CNPYY6","created_at":"2026-07-05T09:42:22Z"},{"alias_kind":"pith_short_8","alias_value":"N2MXQSAI","created_at":"2026-07-05T09:42:22Z"}],"graph_snapshots":[{"event_id":"sha256:727100f71b5aea0daad00d7f64aa0e34707f33f79c8c1e17a568e380824631c0","target":"graph","created_at":"2026-07-05T09:42:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2402.01401/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Alexandra Brintrup, Cengiz \\\"Oztireli, Jack Foster, Kyle Fogarty, Stefan Schoepf, Zack Dugue","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-02T13:33:30Z","title":"An Information Theoretic Approach to Machine Unlearning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.01401","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a0029e5389f46e3d4e367bac2b9f50e5b450379a21bb3dabc01c0e4f2ca0abd5","target":"record","created_at":"2026-07-05T09:42:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"185c3260ba061b239de7f640ad59a0281ed6fe096990e0066c0be2c153eb8173","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-02T13:33:30Z","title_canon_sha256":"32b8014cb276437e1026824e17135bcc36b2d1a1bc4b51694b4cc08eb57ab7dd"},"schema_version":"1.0","source":{"id":"2402.01401","kind":"arxiv","version":4}},"canonical_sha256":"6e99784808d684d7e31e8c9524b2dc1c15427ef83738a6821c698b5c31c659fb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e99784808d684d7e31e8c9524b2dc1c15427ef83738a6821c698b5c31c659fb","first_computed_at":"2026-07-05T09:42:22.513550Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:42:22.513550Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TPrgw0clFx0CUJVaX8KiUs4WrX8jGUXATynAgc9CTyY/K2xxihPMc+OGm0iJYzc9gjlX9ITJp3zujyP5rqF2Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:42:22.514079Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.01401","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a0029e5389f46e3d4e367bac2b9f50e5b450379a21bb3dabc01c0e4f2ca0abd5","sha256:727100f71b5aea0daad00d7f64aa0e34707f33f79c8c1e17a568e380824631c0"],"state_sha256":"1211a747f417eb124bdf5eb68c9ae438d775f0711b364b272fa2ffd7bfcb84ab"}