{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YAPI73K6V7XZTJ7JHEYI53ZDIN","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":"f55c45f25184250e39455c73813cadad94ebcc086a413449ef0f725d6b29e1d3","cross_cats_sorted":["cs.CC","math.ST","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-28T03:22:01Z","title_canon_sha256":"f7e7ffb0b20977c4983aa430d6a284c91ea5d7b6e0b4fb60d4584ad12111b030"},"schema_version":"1.0","source":{"id":"2504.19446","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.19446","created_at":"2026-07-05T10:54:57Z"},{"alias_kind":"arxiv_version","alias_value":"2504.19446v1","created_at":"2026-07-05T10:54:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.19446","created_at":"2026-07-05T10:54:57Z"},{"alias_kind":"pith_short_12","alias_value":"YAPI73K6V7XZ","created_at":"2026-07-05T10:54:57Z"},{"alias_kind":"pith_short_16","alias_value":"YAPI73K6V7XZTJ7J","created_at":"2026-07-05T10:54:57Z"},{"alias_kind":"pith_short_8","alias_value":"YAPI73K6","created_at":"2026-07-05T10:54:57Z"}],"graph_snapshots":[{"event_id":"sha256:9cbcc112081da04c4281d32922a839fd1301086c2a3ef2794c42a331bab5b725","target":"graph","created_at":"2026-07-05T10:54:57Z","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/2504.19446/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We provide efficient algorithms for the problem of distribution learning from high-dimensional Gaussian data where in each sample, some of the variable values are missing. We suppose that the variables are missing not at random (MNAR). The missingness model, denoted by $S(y)$, is the function that maps any point $y$ in $R^d$ to the subsets of its coordinates that are seen. In this work, we assume that it is known. We study the following two settings:\n  (i) Self-censoring: An observation $x$ is generated by first sampling the true value $y$ from a $d$-dimensional Gaussian $N(\\mu*, \\Sigma*)$ wit","authors_text":"Arnab Bhattacharyya, Constantinos Daskalakis, Themis Gouleakis, Yuhao Wang","cross_cats":["cs.CC","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-28T03:22:01Z","title":"Learning High-dimensional Gaussians from Censored Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.19446","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":"sha256:61bea4e86a6fd22f80ba271e547cd10b2f9329d3b87655937214ef58b8f7fdc0","target":"record","created_at":"2026-07-05T10:54:57Z","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":"f55c45f25184250e39455c73813cadad94ebcc086a413449ef0f725d6b29e1d3","cross_cats_sorted":["cs.CC","math.ST","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-28T03:22:01Z","title_canon_sha256":"f7e7ffb0b20977c4983aa430d6a284c91ea5d7b6e0b4fb60d4584ad12111b030"},"schema_version":"1.0","source":{"id":"2504.19446","kind":"arxiv","version":1}},"canonical_sha256":"c01e8fed5eafef99a7e939308eef23436487cd3c6771092f8f851e9dbfc09b36","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c01e8fed5eafef99a7e939308eef23436487cd3c6771092f8f851e9dbfc09b36","first_computed_at":"2026-07-05T10:54:57.707512Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:54:57.707512Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hWGXp6/gt7IXil3cCxVeMJRMKXyow0NcOccxdYdnbKrh2YyfTWnieGgzPMBgwqp7ykw9uBpisIaWohATsBufBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:54:57.708043Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.19446","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:61bea4e86a6fd22f80ba271e547cd10b2f9329d3b87655937214ef58b8f7fdc0","sha256:9cbcc112081da04c4281d32922a839fd1301086c2a3ef2794c42a331bab5b725"],"state_sha256":"b290be1a43d2f8df42141c90a093bf935c7d2d4623da1579eb2ad151f0ea5576"}