{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GC36JPOPPP5EZEFGOMQ2BLQ76P","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":"33c575d55cff64db0bdcc7bd3afabdb037044a11d72e6a2287420b13675b4d87","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-06-18T14:27:44Z","title_canon_sha256":"18f488dca966567ed4add3f9e2a0ca2d7a3a1a3bfd182c354c489f5cbfa7035a"},"schema_version":"1.0","source":{"id":"2406.12659","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.12659","created_at":"2026-07-05T11:51:16Z"},{"alias_kind":"arxiv_version","alias_value":"2406.12659v2","created_at":"2026-07-05T11:51:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.12659","created_at":"2026-07-05T11:51:16Z"},{"alias_kind":"pith_short_12","alias_value":"GC36JPOPPP5E","created_at":"2026-07-05T11:51:16Z"},{"alias_kind":"pith_short_16","alias_value":"GC36JPOPPP5EZEFG","created_at":"2026-07-05T11:51:16Z"},{"alias_kind":"pith_short_8","alias_value":"GC36JPOP","created_at":"2026-07-05T11:51:16Z"}],"graph_snapshots":[{"event_id":"sha256:ad4008589476d5134fc07ce20f7550b8b149c0d39555fe198ca94eed20e1f070","target":"graph","created_at":"2026-07-05T11:51:16Z","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/2406.12659/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a scalable variational Bayes method for statistical inference for a single or low-dimensional subset of the coordinates of a high-dimensional parameter in sparse linear regression. Our approach relies on assigning a mean-field approximation to the nuisance coordinates and carefully modelling the conditional distribution of the target given the nuisance. This requires only a preprocessing step and preserves the computational advantages of mean-field variational Bayes, while ensuring accurate and reliable inference for the target parameter, including for uncertainty quantification. We","authors_text":"Alice L'Huillier, Isma\\\"el Castillo, Kolyan Ray, Luke Travis","cross_cats":["cs.LG","math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-06-18T14:27:44Z","title":"A variational Bayes approach to debiased inference for low-dimensional parameters in high-dimensional linear regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.12659","kind":"arxiv","version":2},"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:f12bf13f5ceca3320d770951a3e932d65f6a1e53f99008e176239a42661f3fb5","target":"record","created_at":"2026-07-05T11:51:16Z","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":"33c575d55cff64db0bdcc7bd3afabdb037044a11d72e6a2287420b13675b4d87","cross_cats_sorted":["cs.LG","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-06-18T14:27:44Z","title_canon_sha256":"18f488dca966567ed4add3f9e2a0ca2d7a3a1a3bfd182c354c489f5cbfa7035a"},"schema_version":"1.0","source":{"id":"2406.12659","kind":"arxiv","version":2}},"canonical_sha256":"30b7e4bdcf7bfa4c90a67321a0ae1ff3df861cb91d0af9de223cbd8a0a6e70c9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"30b7e4bdcf7bfa4c90a67321a0ae1ff3df861cb91d0af9de223cbd8a0a6e70c9","first_computed_at":"2026-07-05T11:51:16.055298Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:51:16.055298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"h1MX1e5k7y/GAnOn7h3UeXkLKozch00xbFXvEKDDDgSdKlNfLG/xpXMXNsO+jTbOwPMWYDWGqIYMHRXVluUBBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:51:16.055805Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.12659","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f12bf13f5ceca3320d770951a3e932d65f6a1e53f99008e176239a42661f3fb5","sha256:ad4008589476d5134fc07ce20f7550b8b149c0d39555fe198ca94eed20e1f070"],"state_sha256":"b623570a3aa42a242ec54c14f0ec2bb6769dbce47dce31ab71f040cf14046d24"}