{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:I7XGSDGJW773TR2IPTQLXYUGTG","short_pith_number":"pith:I7XGSDGJ","schema_version":"1.0","canonical_sha256":"47ee690cc9b7ffb9c7487ce0bbe2869994199cfbba668a2401b1b26794496cd1","source":{"kind":"arxiv","id":"2407.10494","version":1},"attestation_state":"computed","paper":{"title":"Learning to Unlearn for Robust Machine Unlearning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jun Liu, Lin Geng Foo, Mark He Huang","submitted_at":"2024-07-15T07:36:00Z","abstract_excerpt":"Machine unlearning (MU) seeks to remove knowledge of specific data samples from trained models without the necessity for complete retraining, a task made challenging by the dual objectives of effective erasure of data and maintaining the overall performance of the model. Despite recent advances in this field, balancing between the dual objectives of unlearning remains challenging. From a fresh perspective of generalization, we introduce a novel Learning-to-Unlearn (LTU) framework, which adopts a meta-learning approach to optimize the unlearning process to improve forgetting and remembering in "},"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":"2407.10494","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-15T07:36:00Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"d85a981c302831eeaf0c24ff831de69327af05884eaa782373ff73e8691f20cc","abstract_canon_sha256":"11867d7190fe7437a25ec4312ed75948484db652f85fcdb3168a8e099037158a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:00.316767Z","signature_b64":"+28ficq0YVz0uVJBhS+IqBEopyn/Un15rBnOlV8UclV1vwM4bCRvbgnSu74H5r3EMHFIgwryLKhlRSG9qHDrCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47ee690cc9b7ffb9c7487ce0bbe2869994199cfbba668a2401b1b26794496cd1","last_reissued_at":"2026-07-05T08:44:00.316347Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:00.316347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Unlearn for Robust Machine Unlearning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jun Liu, Lin Geng Foo, Mark He Huang","submitted_at":"2024-07-15T07:36:00Z","abstract_excerpt":"Machine unlearning (MU) seeks to remove knowledge of specific data samples from trained models without the necessity for complete retraining, a task made challenging by the dual objectives of effective erasure of data and maintaining the overall performance of the model. Despite recent advances in this field, balancing between the dual objectives of unlearning remains challenging. From a fresh perspective of generalization, we introduce a novel Learning-to-Unlearn (LTU) framework, which adopts a meta-learning approach to optimize the unlearning process to improve forgetting and remembering in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.10494","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/2407.10494/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":"2407.10494","created_at":"2026-07-05T08:44:00.316408+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.10494v1","created_at":"2026-07-05T08:44:00.316408+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.10494","created_at":"2026-07-05T08:44:00.316408+00:00"},{"alias_kind":"pith_short_12","alias_value":"I7XGSDGJW773","created_at":"2026-07-05T08:44:00.316408+00:00"},{"alias_kind":"pith_short_16","alias_value":"I7XGSDGJW773TR2I","created_at":"2026-07-05T08:44:00.316408+00:00"},{"alias_kind":"pith_short_8","alias_value":"I7XGSDGJ","created_at":"2026-07-05T08:44:00.316408+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.04030","citing_title":"Jellyfish: Zero-Shot Federated Unlearning Scheme with Knowledge Disentanglement","ref_index":61,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I7XGSDGJW773TR2IPTQLXYUGTG","json":"https://pith.science/pith/I7XGSDGJW773TR2IPTQLXYUGTG.json","graph_json":"https://pith.science/api/pith-number/I7XGSDGJW773TR2IPTQLXYUGTG/graph.json","events_json":"https://pith.science/api/pith-number/I7XGSDGJW773TR2IPTQLXYUGTG/events.json","paper":"https://pith.science/paper/I7XGSDGJ"},"agent_actions":{"view_html":"https://pith.science/pith/I7XGSDGJW773TR2IPTQLXYUGTG","download_json":"https://pith.science/pith/I7XGSDGJW773TR2IPTQLXYUGTG.json","view_paper":"https://pith.science/paper/I7XGSDGJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.10494&json=true","fetch_graph":"https://pith.science/api/pith-number/I7XGSDGJW773TR2IPTQLXYUGTG/graph.json","fetch_events":"https://pith.science/api/pith-number/I7XGSDGJW773TR2IPTQLXYUGTG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I7XGSDGJW773TR2IPTQLXYUGTG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I7XGSDGJW773TR2IPTQLXYUGTG/action/storage_attestation","attest_author":"https://pith.science/pith/I7XGSDGJW773TR2IPTQLXYUGTG/action/author_attestation","sign_citation":"https://pith.science/pith/I7XGSDGJW773TR2IPTQLXYUGTG/action/citation_signature","submit_replication":"https://pith.science/pith/I7XGSDGJW773TR2IPTQLXYUGTG/action/replication_record"}},"created_at":"2026-07-05T08:44:00.316408+00:00","updated_at":"2026-07-05T08:44:00.316408+00:00"}