{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DHMCYCR35J2RMCTA23MKRV4WMD","short_pith_number":"pith:DHMCYCR3","schema_version":"1.0","canonical_sha256":"19d82c0a3bea75160a60d6d8a8d79660f4e76f42da2ac7bdbfcce6d1944a7c2f","source":{"kind":"arxiv","id":"1810.08361","version":2},"attestation_state":"computed","paper":{"title":"AdaPtive Noisy Data Augmentation (PANDA) for Simultaneous Construction of Multiple Graph Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Fang Liu, Xiao Liu, Yinan Li","submitted_at":"2018-10-19T06:05:30Z","abstract_excerpt":"We extend the data augmentation technique PANDA by Li et al. (2018) that regularizes single graph estimation to jointly learning multiple graphical models with various node types in a unified framework. We design two types of noise to augment the observed data: the first type regularizes the estimation of each graph while the second type promotes either the structural similarity, referred as the \\joint group lasso regularization, or the numerical similarity, referred as the joint fused ridge regularization, among the edges in the same position across graphs. The computation in PANDA is straigh"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1810.08361","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-10-19T06:05:30Z","cross_cats_sorted":[],"title_canon_sha256":"87b2d2f289faf8cd141e81b4c88bbc40bacddd86d105a216124a62a288a55cbd","abstract_canon_sha256":"5dfacaaeb47c8f4d6624c94afb34d85075e74894df4d7c4f81632764e499990f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:26.020716Z","signature_b64":"w7KvTXg8H26KKyIr5kfyJ2Jg7/gbLKz2lbluCs6KOtLrb6B5OVx1vzSHcAnYgqa0WDb4wI2sqkFhqYu8Ud/jAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19d82c0a3bea75160a60d6d8a8d79660f4e76f42da2ac7bdbfcce6d1944a7c2f","last_reissued_at":"2026-05-17T23:45:26.020172Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:26.020172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdaPtive Noisy Data Augmentation (PANDA) for Simultaneous Construction of Multiple Graph Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Fang Liu, Xiao Liu, Yinan Li","submitted_at":"2018-10-19T06:05:30Z","abstract_excerpt":"We extend the data augmentation technique PANDA by Li et al. (2018) that regularizes single graph estimation to jointly learning multiple graphical models with various node types in a unified framework. We design two types of noise to augment the observed data: the first type regularizes the estimation of each graph while the second type promotes either the structural similarity, referred as the \\joint group lasso regularization, or the numerical similarity, referred as the joint fused ridge regularization, among the edges in the same position across graphs. The computation in PANDA is straigh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.08361","kind":"arxiv","version":2},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1810.08361","created_at":"2026-05-17T23:45:26.020265+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.08361v2","created_at":"2026-05-17T23:45:26.020265+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.08361","created_at":"2026-05-17T23:45:26.020265+00:00"},{"alias_kind":"pith_short_12","alias_value":"DHMCYCR35J2R","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DHMCYCR35J2RMCTA","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DHMCYCR3","created_at":"2026-05-18T12:32:19.392346+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DHMCYCR35J2RMCTA23MKRV4WMD","json":"https://pith.science/pith/DHMCYCR35J2RMCTA23MKRV4WMD.json","graph_json":"https://pith.science/api/pith-number/DHMCYCR35J2RMCTA23MKRV4WMD/graph.json","events_json":"https://pith.science/api/pith-number/DHMCYCR35J2RMCTA23MKRV4WMD/events.json","paper":"https://pith.science/paper/DHMCYCR3"},"agent_actions":{"view_html":"https://pith.science/pith/DHMCYCR35J2RMCTA23MKRV4WMD","download_json":"https://pith.science/pith/DHMCYCR35J2RMCTA23MKRV4WMD.json","view_paper":"https://pith.science/paper/DHMCYCR3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.08361&json=true","fetch_graph":"https://pith.science/api/pith-number/DHMCYCR35J2RMCTA23MKRV4WMD/graph.json","fetch_events":"https://pith.science/api/pith-number/DHMCYCR35J2RMCTA23MKRV4WMD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DHMCYCR35J2RMCTA23MKRV4WMD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DHMCYCR35J2RMCTA23MKRV4WMD/action/storage_attestation","attest_author":"https://pith.science/pith/DHMCYCR35J2RMCTA23MKRV4WMD/action/author_attestation","sign_citation":"https://pith.science/pith/DHMCYCR35J2RMCTA23MKRV4WMD/action/citation_signature","submit_replication":"https://pith.science/pith/DHMCYCR35J2RMCTA23MKRV4WMD/action/replication_record"}},"created_at":"2026-05-17T23:45:26.020265+00:00","updated_at":"2026-05-17T23:45:26.020265+00:00"}