{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:MQ5NK2GORAGKHUTDHQZZEZ6XJH","short_pith_number":"pith:MQ5NK2GO","canonical_record":{"source":{"id":"2410.01093","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-10-01T21:41:21Z","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"title_canon_sha256":"8fb15bb669ba124f20e5ebfd1f1f6055ede4b735d0d0d4d444854d0d0afd2b3c","abstract_canon_sha256":"636e43fb70804d25f4c07224e1acefac9da7cd97acf9fcc680f85dbf1a852cc6"},"schema_version":"1.0"},"canonical_sha256":"643ad568ce880ca3d2633c339267d749da6035cf4daadd52c8022858263fb90a","source":{"kind":"arxiv","id":"2410.01093","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.01093","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"arxiv_version","alias_value":"2410.01093v1","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01093","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"pith_short_12","alias_value":"MQ5NK2GORAGK","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"pith_short_16","alias_value":"MQ5NK2GORAGKHUTD","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"pith_short_8","alias_value":"MQ5NK2GO","created_at":"2026-07-05T09:14:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:MQ5NK2GORAGKHUTDHQZZEZ6XJH","target":"record","payload":{"canonical_record":{"source":{"id":"2410.01093","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-10-01T21:41:21Z","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"title_canon_sha256":"8fb15bb669ba124f20e5ebfd1f1f6055ede4b735d0d0d4d444854d0d0afd2b3c","abstract_canon_sha256":"636e43fb70804d25f4c07224e1acefac9da7cd97acf9fcc680f85dbf1a852cc6"},"schema_version":"1.0"},"canonical_sha256":"643ad568ce880ca3d2633c339267d749da6035cf4daadd52c8022858263fb90a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:14:43.220947Z","signature_b64":"8p4uJ9assdTbSiAjDVLKu0cFZ+hbGm5mM8zOcEuF6Bxtcan+i7qrbyt6L5YliYD3sOshhwP1bvdzS1mcT9thCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"643ad568ce880ca3d2633c339267d749da6035cf4daadd52c8022858263fb90a","last_reissued_at":"2026-07-05T09:14:43.220479Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:14:43.220479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.01093","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":"2026-07-05T09:14:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7/st6bCpurVrD2y1RP4SKEnX9GjClFiuKJEFtjaFdJ+kOSXxPUNkh9CKasu+c+xg5euZYyQHQsMsHmpiEW55Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:25:16.962153Z"},"content_sha256":"5757cb561422d85d2929ff6d099684f6220dc84d30c3906467af92625ba18904","schema_version":"1.0","event_id":"sha256:5757cb561422d85d2929ff6d099684f6220dc84d30c3906467af92625ba18904"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:MQ5NK2GORAGKHUTDHQZZEZ6XJH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-dimensional logistic regression with missing data: Imputation, regularization, and universality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Andrea Montanari, Kabir Aladin Verchand","submitted_at":"2024-10-01T21:41:21Z","abstract_excerpt":"We study high-dimensional, ridge-regularized logistic regression in a setting in which the covariates may be missing or corrupted by additive noise. When both the covariates and the additive corruptions are independent and normally distributed, we provide exact characterizations of both the prediction error as well as the estimation error. Moreover, we show that these characterizations are universal: as long as the entries of the data matrix satisfy a set of independence and moment conditions, our guarantees continue to hold. Universality, in turn, enables the detailed study of several imputat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01093","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/2410.01093/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":"2026-07-05T09:14:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PnLWK5lsfa6ySLUCG3SyCpoiPUJzMGGX9mijROuC2R/5yUI3sFQm1IhBlpsaCBtDXT2ATF/yn/bz93FlAE1uAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T21:25:16.962711Z"},"content_sha256":"648ebd58ecc7dfa8036a702f92fccf828a2a716bb72b2e3a8908b9e4a2cfd398","schema_version":"1.0","event_id":"sha256:648ebd58ecc7dfa8036a702f92fccf828a2a716bb72b2e3a8908b9e4a2cfd398"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MQ5NK2GORAGKHUTDHQZZEZ6XJH/bundle.json","state_url":"https://pith.science/pith/MQ5NK2GORAGKHUTDHQZZEZ6XJH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MQ5NK2GORAGKHUTDHQZZEZ6XJH/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":"2026-08-18T21:25:16Z","links":{"resolver":"https://pith.science/pith/MQ5NK2GORAGKHUTDHQZZEZ6XJH","bundle":"https://pith.science/pith/MQ5NK2GORAGKHUTDHQZZEZ6XJH/bundle.json","state":"https://pith.science/pith/MQ5NK2GORAGKHUTDHQZZEZ6XJH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MQ5NK2GORAGKHUTDHQZZEZ6XJH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:MQ5NK2GORAGKHUTDHQZZEZ6XJH","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":"636e43fb70804d25f4c07224e1acefac9da7cd97acf9fcc680f85dbf1a852cc6","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-10-01T21:41:21Z","title_canon_sha256":"8fb15bb669ba124f20e5ebfd1f1f6055ede4b735d0d0d4d444854d0d0afd2b3c"},"schema_version":"1.0","source":{"id":"2410.01093","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.01093","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"arxiv_version","alias_value":"2410.01093v1","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01093","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"pith_short_12","alias_value":"MQ5NK2GORAGK","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"pith_short_16","alias_value":"MQ5NK2GORAGKHUTD","created_at":"2026-07-05T09:14:43Z"},{"alias_kind":"pith_short_8","alias_value":"MQ5NK2GO","created_at":"2026-07-05T09:14:43Z"}],"graph_snapshots":[{"event_id":"sha256:648ebd58ecc7dfa8036a702f92fccf828a2a716bb72b2e3a8908b9e4a2cfd398","target":"graph","created_at":"2026-07-05T09:14:43Z","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/2410.01093/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study high-dimensional, ridge-regularized logistic regression in a setting in which the covariates may be missing or corrupted by additive noise. When both the covariates and the additive corruptions are independent and normally distributed, we provide exact characterizations of both the prediction error as well as the estimation error. Moreover, we show that these characterizations are universal: as long as the entries of the data matrix satisfy a set of independence and moment conditions, our guarantees continue to hold. Universality, in turn, enables the detailed study of several imputat","authors_text":"Andrea Montanari, Kabir Aladin Verchand","cross_cats":["cs.LG","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-10-01T21:41:21Z","title":"High-dimensional logistic regression with missing data: Imputation, regularization, and universality"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01093","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":"sha256:5757cb561422d85d2929ff6d099684f6220dc84d30c3906467af92625ba18904","target":"record","created_at":"2026-07-05T09:14:43Z","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":"636e43fb70804d25f4c07224e1acefac9da7cd97acf9fcc680f85dbf1a852cc6","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-10-01T21:41:21Z","title_canon_sha256":"8fb15bb669ba124f20e5ebfd1f1f6055ede4b735d0d0d4d444854d0d0afd2b3c"},"schema_version":"1.0","source":{"id":"2410.01093","kind":"arxiv","version":1}},"canonical_sha256":"643ad568ce880ca3d2633c339267d749da6035cf4daadd52c8022858263fb90a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"643ad568ce880ca3d2633c339267d749da6035cf4daadd52c8022858263fb90a","first_computed_at":"2026-07-05T09:14:43.220479Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:14:43.220479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8p4uJ9assdTbSiAjDVLKu0cFZ+hbGm5mM8zOcEuF6Bxtcan+i7qrbyt6L5YliYD3sOshhwP1bvdzS1mcT9thCA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:14:43.220947Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.01093","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5757cb561422d85d2929ff6d099684f6220dc84d30c3906467af92625ba18904","sha256:648ebd58ecc7dfa8036a702f92fccf828a2a716bb72b2e3a8908b9e4a2cfd398"],"state_sha256":"f269701e14832d62125aa0ef370a1dbb6512f1a449ff7f22d64569feb2f643f8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7wZdI59sbnUmNBzdWLTF0Ajgyr/qlX7+pdHKnMPr/euTEMYxxodtukhFSxbTzO+e9MAbp1O427ZEe7drhUcwBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T21:25:16.966182Z","bundle_sha256":"1f1c732fa232e21caab574c36f361a074841f6b6d591e511281bb2ab2c896eb9"}}