{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:JUV4NOQED2OKTTUJUYLX7TTLBQ","short_pith_number":"pith:JUV4NOQE","canonical_record":{"source":{"id":"2410.09311","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-10-12T00:20:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1141e00fd058cc7135c7b7a944ba108112da6f7bb3d9501121922e5b7625ca80","abstract_canon_sha256":"7b152d404b82be1df9fe8a6318da7b9a51cf66c0b419bf5d49fae0ddadcde891"},"schema_version":"1.0"},"canonical_sha256":"4d2bc6ba041e9ca9ce89a6177fce6b0c34fea832402719595491d8e3dde35090","source":{"kind":"arxiv","id":"2410.09311","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.09311","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"arxiv_version","alias_value":"2410.09311v1","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09311","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"pith_short_12","alias_value":"JUV4NOQED2OK","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"pith_short_16","alias_value":"JUV4NOQED2OKTTUJ","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"pith_short_8","alias_value":"JUV4NOQE","created_at":"2026-07-05T09:19:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:JUV4NOQED2OKTTUJUYLX7TTLBQ","target":"record","payload":{"canonical_record":{"source":{"id":"2410.09311","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-10-12T00:20:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1141e00fd058cc7135c7b7a944ba108112da6f7bb3d9501121922e5b7625ca80","abstract_canon_sha256":"7b152d404b82be1df9fe8a6318da7b9a51cf66c0b419bf5d49fae0ddadcde891"},"schema_version":"1.0"},"canonical_sha256":"4d2bc6ba041e9ca9ce89a6177fce6b0c34fea832402719595491d8e3dde35090","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:19:37.547789Z","signature_b64":"hwCzDziYYqXBYUxvb+lHEzyMOoMee5DfjF+GTuiScxaAqwpjqcsCv9qXJY2BJRpp7FDTYiV6TmzHPixQhIiGAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d2bc6ba041e9ca9ce89a6177fce6b0c34fea832402719595491d8e3dde35090","last_reissued_at":"2026-07-05T09:19:37.547450Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:19:37.547450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.09311","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:19:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7FMZY/IFJHExbefLeOBUAVUnwJufcp6yEOxU2Xs7kcoWo/2uY3ha+Vu8WbC9ERE2im0wWGF8mwLDPydQnAgcDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:35:29.958747Z"},"content_sha256":"2a335ec8a8cd81eff4c4eaee1dc18b281cb65d7880fb286c38a51c048414c085","schema_version":"1.0","event_id":"sha256:2a335ec8a8cd81eff4c4eaee1dc18b281cb65d7880fb286c38a51c048414c085"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:JUV4NOQED2OKTTUJUYLX7TTLBQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data Deletion for Linear Regression with Noisy SGD","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chi-Hua Wang, Guang Cheng, Zhangjie Xia","submitted_at":"2024-10-12T00:20:26Z","abstract_excerpt":"In the current era of big data and machine learning, it's essential to find ways to shrink the size of training dataset while preserving the training performance to improve efficiency. However, the challenge behind it includes providing practical ways to find points that can be deleted without significantly harming the training result and suffering from problems like underfitting. We therefore present the perfect deleted point problem for 1-step noisy SGD in the classical linear regression task, which aims to find the perfect deleted point in the training dataset such that the model resulted f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09311","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/2410.09311/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:19:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1SvZa/h681akQEGcIjF5AiHDfbQN/rrHZuIcgaUKevkbtbfyqZoM3EIgMSLMZFMa+D6b/o7PW5jYnyA0IHcrBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T15:35:29.959687Z"},"content_sha256":"f8d18eaad87fc0c9ef8571c41bf8d675eef7172ccc3bad0659c49779454690ce","schema_version":"1.0","event_id":"sha256:f8d18eaad87fc0c9ef8571c41bf8d675eef7172ccc3bad0659c49779454690ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JUV4NOQED2OKTTUJUYLX7TTLBQ/bundle.json","state_url":"https://pith.science/pith/JUV4NOQED2OKTTUJUYLX7TTLBQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JUV4NOQED2OKTTUJUYLX7TTLBQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T15:35:29Z","links":{"resolver":"https://pith.science/pith/JUV4NOQED2OKTTUJUYLX7TTLBQ","bundle":"https://pith.science/pith/JUV4NOQED2OKTTUJUYLX7TTLBQ/bundle.json","state":"https://pith.science/pith/JUV4NOQED2OKTTUJUYLX7TTLBQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JUV4NOQED2OKTTUJUYLX7TTLBQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JUV4NOQED2OKTTUJUYLX7TTLBQ","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":"7b152d404b82be1df9fe8a6318da7b9a51cf66c0b419bf5d49fae0ddadcde891","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-10-12T00:20:26Z","title_canon_sha256":"1141e00fd058cc7135c7b7a944ba108112da6f7bb3d9501121922e5b7625ca80"},"schema_version":"1.0","source":{"id":"2410.09311","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.09311","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"arxiv_version","alias_value":"2410.09311v1","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09311","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"pith_short_12","alias_value":"JUV4NOQED2OK","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"pith_short_16","alias_value":"JUV4NOQED2OKTTUJ","created_at":"2026-07-05T09:19:37Z"},{"alias_kind":"pith_short_8","alias_value":"JUV4NOQE","created_at":"2026-07-05T09:19:37Z"}],"graph_snapshots":[{"event_id":"sha256:f8d18eaad87fc0c9ef8571c41bf8d675eef7172ccc3bad0659c49779454690ce","target":"graph","created_at":"2026-07-05T09:19:37Z","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/2410.09311/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the current era of big data and machine learning, it's essential to find ways to shrink the size of training dataset while preserving the training performance to improve efficiency. However, the challenge behind it includes providing practical ways to find points that can be deleted without significantly harming the training result and suffering from problems like underfitting. We therefore present the perfect deleted point problem for 1-step noisy SGD in the classical linear regression task, which aims to find the perfect deleted point in the training dataset such that the model resulted f","authors_text":"Chi-Hua Wang, Guang Cheng, Zhangjie Xia","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-10-12T00:20:26Z","title":"Data Deletion for Linear Regression with Noisy SGD"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09311","kind":"arxiv","version":1},"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":"sha256:2a335ec8a8cd81eff4c4eaee1dc18b281cb65d7880fb286c38a51c048414c085","target":"record","created_at":"2026-07-05T09:19:37Z","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":"7b152d404b82be1df9fe8a6318da7b9a51cf66c0b419bf5d49fae0ddadcde891","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-10-12T00:20:26Z","title_canon_sha256":"1141e00fd058cc7135c7b7a944ba108112da6f7bb3d9501121922e5b7625ca80"},"schema_version":"1.0","source":{"id":"2410.09311","kind":"arxiv","version":1}},"canonical_sha256":"4d2bc6ba041e9ca9ce89a6177fce6b0c34fea832402719595491d8e3dde35090","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4d2bc6ba041e9ca9ce89a6177fce6b0c34fea832402719595491d8e3dde35090","first_computed_at":"2026-07-05T09:19:37.547450Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:19:37.547450Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hwCzDziYYqXBYUxvb+lHEzyMOoMee5DfjF+GTuiScxaAqwpjqcsCv9qXJY2BJRpp7FDTYiV6TmzHPixQhIiGAw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:19:37.547789Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.09311","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2a335ec8a8cd81eff4c4eaee1dc18b281cb65d7880fb286c38a51c048414c085","sha256:f8d18eaad87fc0c9ef8571c41bf8d675eef7172ccc3bad0659c49779454690ce"],"state_sha256":"e45a3f5071e1af5fe85194f796eaa1ea4b03a2cfceee696c6296549529abac2a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VbZJEb7F5WgEq0AgdFMLklXoAAdR5qVKfO+f/iHb+9a38z+6XYDxyabR6SnWg5qHlTe4F4lIngW5x9qQ/LpuAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T15:35:29.973206Z","bundle_sha256":"f0592e834c0a3299444bd2fe14b9fbb74c508ba0a1303361f0e9523a81781d26"}}