{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EG5JVABIPB3U6VOSXGDPF3YUOW","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":"66edcc0f0ff3e57eca4a35938000147f9b6adf885c79ec5a43ebb5692591c21a","cross_cats_sorted":["cs.IT","cs.LG","eess.SP","math.IT","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-01-08T14:34:35Z","title_canon_sha256":"d0f48373e43a5ea5d185264554e2654fa1fbffee7993bcc93644333b483931d1"},"schema_version":"1.0","source":{"id":"2401.03923","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.03923","created_at":"2026-07-05T07:31:15Z"},{"alias_kind":"arxiv_version","alias_value":"2401.03923v1","created_at":"2026-07-05T07:31:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.03923","created_at":"2026-07-05T07:31:15Z"},{"alias_kind":"pith_short_12","alias_value":"EG5JVABIPB3U","created_at":"2026-07-05T07:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"EG5JVABIPB3U6VOS","created_at":"2026-07-05T07:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"EG5JVABI","created_at":"2026-07-05T07:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:d84824ed8b343aa04d46a20b9289a15b841108a02214d35d0669235338360e51","target":"graph","created_at":"2026-07-05T07:31:15Z","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/2401.03923/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Characterizing the distribution of high-dimensional statistical estimators is a challenging task, due to the breakdown of classical asymptotic theory in high dimension. This paper makes progress towards this by developing non-asymptotic distributional characterizations for approximate message passing (AMP) -- a family of iterative algorithms that prove effective as both fast estimators and powerful theoretical machinery -- for both sparse and robust regression. Prior AMP theory, which focused on high-dimensional asymptotics for the most part, failed to describe the behavior of AMP when the num","authors_text":"Gen Li, Yuting Wei","cross_cats":["cs.IT","cs.LG","eess.SP","math.IT","stat.ML","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-01-08T14:34:35Z","title":"A non-asymptotic distributional theory of approximate message passing for sparse and robust regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.03923","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":"sha256:3d9cf310529cdbe2544461190bccf1c49a3333f3a704477173d6ad3ba1e62474","target":"record","created_at":"2026-07-05T07:31:15Z","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":"66edcc0f0ff3e57eca4a35938000147f9b6adf885c79ec5a43ebb5692591c21a","cross_cats_sorted":["cs.IT","cs.LG","eess.SP","math.IT","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-01-08T14:34:35Z","title_canon_sha256":"d0f48373e43a5ea5d185264554e2654fa1fbffee7993bcc93644333b483931d1"},"schema_version":"1.0","source":{"id":"2401.03923","kind":"arxiv","version":1}},"canonical_sha256":"21ba9a802878774f55d2b986f2ef1475b878e83270058fdb4f8357e02745ad49","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"21ba9a802878774f55d2b986f2ef1475b878e83270058fdb4f8357e02745ad49","first_computed_at":"2026-07-05T07:31:15.407416Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:31:15.407416Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2C378t00JYWKksHeo3HxNkntg2wNUFjhPvA5GOkC7A4otR4wCA4HJIxXsZWxMyaklydcpx9Nu8Vrb6/QbT0qDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:31:15.407845Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.03923","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d9cf310529cdbe2544461190bccf1c49a3333f3a704477173d6ad3ba1e62474","sha256:d84824ed8b343aa04d46a20b9289a15b841108a02214d35d0669235338360e51"],"state_sha256":"6faa72afd49df553742bf2597b35c0a1ff593dd2bc0ca42f6242731ff887f5ca"}