{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7GA7UX4FFOCTH3NOYAWVSICEHB","short_pith_number":"pith:7GA7UX4F","schema_version":"1.0","canonical_sha256":"f981fa5f852b8533edaec02d592044385eb049e5081a92b25ed0803a5bc0f1c7","source":{"kind":"arxiv","id":"2210.17312","version":6},"attestation_state":"computed","paper":{"title":"Neural network-based CUSUM for online change-point detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Junghwan Lee, Tingnan Gong, Xiuyuan Cheng, Yao Xie","submitted_at":"2022-10-31T16:47:11Z","abstract_excerpt":"Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning. CUSUM is a popular statistical method for online change-point detection due to its efficiency from recursive computation and constant memory requirement, and it enjoys statistical optimality. CUSUM requires knowing the precise pre- and post-change distribution. However, post-change distribution is usually unknown a priori since it represents anomaly and novelty. Classic CUSUM can perform poorly when there is a model mismatch with actual "},"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":"2210.17312","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2022-10-31T16:47:11Z","cross_cats_sorted":["stat.ME","stat.ML"],"title_canon_sha256":"c5e94b4273e3ee3fafa6952563046c191684f1451d5e07ad71b809c487ebf2be","abstract_canon_sha256":"e03d1bb81eea6544c6b3d75feaa5e36a8bedb4e9cec5e2fe81af367ad74ca32c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:53:58.424992Z","signature_b64":"oEUQiEE5lUcsgERD32M9doOl2UEwufCxagVp6z0i5W3szWEK5no2Y+08ri0THFLKDk77cA5taj1E5vDQLdWQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f981fa5f852b8533edaec02d592044385eb049e5081a92b25ed0803a5bc0f1c7","last_reissued_at":"2026-07-05T07:53:58.424596Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:53:58.424596Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural network-based CUSUM for online change-point detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Junghwan Lee, Tingnan Gong, Xiuyuan Cheng, Yao Xie","submitted_at":"2022-10-31T16:47:11Z","abstract_excerpt":"Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning. CUSUM is a popular statistical method for online change-point detection due to its efficiency from recursive computation and constant memory requirement, and it enjoys statistical optimality. CUSUM requires knowing the precise pre- and post-change distribution. However, post-change distribution is usually unknown a priori since it represents anomaly and novelty. Classic CUSUM can perform poorly when there is a model mismatch with actual "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.17312","kind":"arxiv","version":6},"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/2210.17312/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":"2210.17312","created_at":"2026-07-05T07:53:58.424653+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.17312v6","created_at":"2026-07-05T07:53:58.424653+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.17312","created_at":"2026-07-05T07:53:58.424653+00:00"},{"alias_kind":"pith_short_12","alias_value":"7GA7UX4FFOCT","created_at":"2026-07-05T07:53:58.424653+00:00"},{"alias_kind":"pith_short_16","alias_value":"7GA7UX4FFOCTH3NO","created_at":"2026-07-05T07:53:58.424653+00:00"},{"alias_kind":"pith_short_8","alias_value":"7GA7UX4F","created_at":"2026-07-05T07:53:58.424653+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12476","citing_title":"Quickest Detection of Hallucination Onset: Delay Bounds and Learned CUSUM Statistics","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09737","citing_title":"Online change point detection under heavy-tailedness and contamination","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05090","citing_title":"Bernoulli CUSUM and Bayes-Optimal Detection Ceilings for Trust Fraud in Sparse Rating Networks","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22886","citing_title":"Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7GA7UX4FFOCTH3NOYAWVSICEHB","json":"https://pith.science/pith/7GA7UX4FFOCTH3NOYAWVSICEHB.json","graph_json":"https://pith.science/api/pith-number/7GA7UX4FFOCTH3NOYAWVSICEHB/graph.json","events_json":"https://pith.science/api/pith-number/7GA7UX4FFOCTH3NOYAWVSICEHB/events.json","paper":"https://pith.science/paper/7GA7UX4F"},"agent_actions":{"view_html":"https://pith.science/pith/7GA7UX4FFOCTH3NOYAWVSICEHB","download_json":"https://pith.science/pith/7GA7UX4FFOCTH3NOYAWVSICEHB.json","view_paper":"https://pith.science/paper/7GA7UX4F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.17312&json=true","fetch_graph":"https://pith.science/api/pith-number/7GA7UX4FFOCTH3NOYAWVSICEHB/graph.json","fetch_events":"https://pith.science/api/pith-number/7GA7UX4FFOCTH3NOYAWVSICEHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7GA7UX4FFOCTH3NOYAWVSICEHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7GA7UX4FFOCTH3NOYAWVSICEHB/action/storage_attestation","attest_author":"https://pith.science/pith/7GA7UX4FFOCTH3NOYAWVSICEHB/action/author_attestation","sign_citation":"https://pith.science/pith/7GA7UX4FFOCTH3NOYAWVSICEHB/action/citation_signature","submit_replication":"https://pith.science/pith/7GA7UX4FFOCTH3NOYAWVSICEHB/action/replication_record"}},"created_at":"2026-07-05T07:53:58.424653+00:00","updated_at":"2026-07-05T07:53:58.424653+00:00"}