{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:KZLJTUR4SZSDBQ53NK5IEQYRPO","short_pith_number":"pith:KZLJTUR4","canonical_record":{"source":{"id":"1209.6419","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-09-28T04:12:14Z","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"title_canon_sha256":"51c5beabe6126886d07050b4683c84e1ada2fcf89b6d305da0a308aaae7d0fa1","abstract_canon_sha256":"343b617da2162b3c27f7d970c9d10f061e2b3447f84eb65ec2efb2b70ed3c22f"},"schema_version":"1.0"},"canonical_sha256":"565699d23c966430c3bb6aba8243117ba0359d241e73a5540ee6f20043016210","source":{"kind":"arxiv","id":"1209.6419","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1209.6419","created_at":"2026-05-18T03:44:25Z"},{"alias_kind":"arxiv_version","alias_value":"1209.6419v1","created_at":"2026-05-18T03:44:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.6419","created_at":"2026-05-18T03:44:25Z"},{"alias_kind":"pith_short_12","alias_value":"KZLJTUR4SZSD","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"KZLJTUR4SZSDBQ53","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"KZLJTUR4","created_at":"2026-05-18T12:27:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:KZLJTUR4SZSDBQ53NK5IEQYRPO","target":"record","payload":{"canonical_record":{"source":{"id":"1209.6419","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-09-28T04:12:14Z","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"title_canon_sha256":"51c5beabe6126886d07050b4683c84e1ada2fcf89b6d305da0a308aaae7d0fa1","abstract_canon_sha256":"343b617da2162b3c27f7d970c9d10f061e2b3447f84eb65ec2efb2b70ed3c22f"},"schema_version":"1.0"},"canonical_sha256":"565699d23c966430c3bb6aba8243117ba0359d241e73a5540ee6f20043016210","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:44:25.528782Z","signature_b64":"4VhIfSMw+W1BWnj1KNrRykvcPPeuNHyeKt/Xyr6Yt2DQoVPbBXFGEDKt8z4TIot/dO7F14CWuDvquuTQk6ZmBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"565699d23c966430c3bb6aba8243117ba0359d241e73a5540ee6f20043016210","last_reissued_at":"2026-05-18T03:44:25.528183Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:44:25.528183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1209.6419","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":"2026-05-18T03:44:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/nWCbXe25Lgo8S0nfeagKbvMk3EyRq9W2xBRbnn9Y84JDyicgWkyO8dwTjqUDwxkA7ud0oDLVBEnz8etSRATAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:31:45.502248Z"},"content_sha256":"4cc6e4d7a4fd63039c00643a80421112a3c587fedb467d7edcc198ad5bb73b4e","schema_version":"1.0","event_id":"sha256:4cc6e4d7a4fd63039c00643a80421112a3c587fedb467d7edcc198ad5bb73b4e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:KZLJTUR4SZSDBQ53NK5IEQYRPO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Partial Gaussian Graphical Model Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Tong Zhang, Xiao-Tong Yuan","submitted_at":"2012-09-28T04:12:14Z","abstract_excerpt":"This paper studies the partial estimation of Gaussian graphical models from high-dimensional empirical observations. We derive a convex formulation for this problem using $\\ell_1$-regularized maximum-likelihood estimation, which can be solved via a block coordinate descent algorithm. Statistical estimation performance can be established for our method. The proposed approach has competitive empirical performance compared to existing methods, as demonstrated by various experiments on synthetic and real datasets."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.6419","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":""},"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":"2026-05-18T03:44:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y0Evmv834tVaSmOhO+4kzfxEtiubYoqkojWnpsccXpi3s5CZuxxpIppSQsZN5WeXdS/BtJjsPp/ujg8j8X8NDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:31:45.502651Z"},"content_sha256":"414b02016705b94b993177d1f1f50e405f5f3f9cdd7b18d18a3220391b1c01a5","schema_version":"1.0","event_id":"sha256:414b02016705b94b993177d1f1f50e405f5f3f9cdd7b18d18a3220391b1c01a5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KZLJTUR4SZSDBQ53NK5IEQYRPO/bundle.json","state_url":"https://pith.science/pith/KZLJTUR4SZSDBQ53NK5IEQYRPO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KZLJTUR4SZSDBQ53NK5IEQYRPO/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":"2026-06-04T01:31:45Z","links":{"resolver":"https://pith.science/pith/KZLJTUR4SZSDBQ53NK5IEQYRPO","bundle":"https://pith.science/pith/KZLJTUR4SZSDBQ53NK5IEQYRPO/bundle.json","state":"https://pith.science/pith/KZLJTUR4SZSDBQ53NK5IEQYRPO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KZLJTUR4SZSDBQ53NK5IEQYRPO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:KZLJTUR4SZSDBQ53NK5IEQYRPO","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":"343b617da2162b3c27f7d970c9d10f061e2b3447f84eb65ec2efb2b70ed3c22f","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-09-28T04:12:14Z","title_canon_sha256":"51c5beabe6126886d07050b4683c84e1ada2fcf89b6d305da0a308aaae7d0fa1"},"schema_version":"1.0","source":{"id":"1209.6419","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1209.6419","created_at":"2026-05-18T03:44:25Z"},{"alias_kind":"arxiv_version","alias_value":"1209.6419v1","created_at":"2026-05-18T03:44:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.6419","created_at":"2026-05-18T03:44:25Z"},{"alias_kind":"pith_short_12","alias_value":"KZLJTUR4SZSD","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_16","alias_value":"KZLJTUR4SZSDBQ53","created_at":"2026-05-18T12:27:11Z"},{"alias_kind":"pith_short_8","alias_value":"KZLJTUR4","created_at":"2026-05-18T12:27:11Z"}],"graph_snapshots":[{"event_id":"sha256:414b02016705b94b993177d1f1f50e405f5f3f9cdd7b18d18a3220391b1c01a5","target":"graph","created_at":"2026-05-18T03:44:25Z","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"},"paper":{"abstract_excerpt":"This paper studies the partial estimation of Gaussian graphical models from high-dimensional empirical observations. We derive a convex formulation for this problem using $\\ell_1$-regularized maximum-likelihood estimation, which can be solved via a block coordinate descent algorithm. Statistical estimation performance can be established for our method. The proposed approach has competitive empirical performance compared to existing methods, as demonstrated by various experiments on synthetic and real datasets.","authors_text":"Tong Zhang, Xiao-Tong Yuan","cross_cats":["cs.IT","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-09-28T04:12:14Z","title":"Partial Gaussian Graphical Model Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.6419","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":"sha256:4cc6e4d7a4fd63039c00643a80421112a3c587fedb467d7edcc198ad5bb73b4e","target":"record","created_at":"2026-05-18T03:44:25Z","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":"343b617da2162b3c27f7d970c9d10f061e2b3447f84eb65ec2efb2b70ed3c22f","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-09-28T04:12:14Z","title_canon_sha256":"51c5beabe6126886d07050b4683c84e1ada2fcf89b6d305da0a308aaae7d0fa1"},"schema_version":"1.0","source":{"id":"1209.6419","kind":"arxiv","version":1}},"canonical_sha256":"565699d23c966430c3bb6aba8243117ba0359d241e73a5540ee6f20043016210","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"565699d23c966430c3bb6aba8243117ba0359d241e73a5540ee6f20043016210","first_computed_at":"2026-05-18T03:44:25.528183Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:44:25.528183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4VhIfSMw+W1BWnj1KNrRykvcPPeuNHyeKt/Xyr6Yt2DQoVPbBXFGEDKt8z4TIot/dO7F14CWuDvquuTQk6ZmBg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:44:25.528782Z","signed_message":"canonical_sha256_bytes"},"source_id":"1209.6419","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4cc6e4d7a4fd63039c00643a80421112a3c587fedb467d7edcc198ad5bb73b4e","sha256:414b02016705b94b993177d1f1f50e405f5f3f9cdd7b18d18a3220391b1c01a5"],"state_sha256":"2ecb392000b92da0b39dfd75f9efc05f495dfac273a541c16c6cb95706c582e2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OjKrT5R1nMstHEqUGwgAhUJQ0W/Ce/W3wRzMYX4JKWyEs8Gv1xzGbGb+k9LgxHochkZ7DZJ1qHytLwC1MBaCAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T01:31:45.504851Z","bundle_sha256":"3c1386d721f517deb1f9873fff04ba55879ddda20cd8087e24f3e0aa4f24eb5a"}}