{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WWBUUCSBFUAEDBOKTR4YLHW2DT","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":"c07fb7898e7636b729557e58b57a258b48c2d02c1e01c8cb7d184d0d61af007a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-02T03:49:47Z","title_canon_sha256":"a29c7a72839bb5276a7523e7e865bafd3daa2894ee434df11da9f88f9d0469f1"},"schema_version":"1.0","source":{"id":"2307.00467","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.00467","created_at":"2026-07-05T06:26:56Z"},{"alias_kind":"arxiv_version","alias_value":"2307.00467v1","created_at":"2026-07-05T06:26:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.00467","created_at":"2026-07-05T06:26:56Z"},{"alias_kind":"pith_short_12","alias_value":"WWBUUCSBFUAE","created_at":"2026-07-05T06:26:56Z"},{"alias_kind":"pith_short_16","alias_value":"WWBUUCSBFUAEDBOK","created_at":"2026-07-05T06:26:56Z"},{"alias_kind":"pith_short_8","alias_value":"WWBUUCSB","created_at":"2026-07-05T06:26:56Z"}],"graph_snapshots":[{"event_id":"sha256:05d61de996c35844fcc18de8e927be8c8c4be2aacce4b5a5a00cbc0118da35ae","target":"graph","created_at":"2026-07-05T06:26:56Z","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/2307.00467/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The diffusion model has shown remarkable performance in modeling data distributions and synthesizing data. However, the vanilla diffusion model requires complete or fully observed data for training. Incomplete data is a common issue in various real-world applications, including healthcare and finance, particularly when dealing with tabular datasets. This work presents a unified and principled diffusion-based framework for learning from data with missing values under various missing mechanisms. We first observe that the widely adopted \"impute-then-generate\" pipeline may lead to a biased learnin","authors_text":"Chongxuan Li, Guang Cheng, Liyan Xie, Yidong Ouyang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-02T03:49:47Z","title":"MissDiff: Training Diffusion Models on Tabular Data with Missing Values"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.00467","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":"sha256:10ac4b487ccbbb8e30958a81b48c646545e83b3805152e567abcc289f637d2ce","target":"record","created_at":"2026-07-05T06:26:56Z","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":"c07fb7898e7636b729557e58b57a258b48c2d02c1e01c8cb7d184d0d61af007a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-02T03:49:47Z","title_canon_sha256":"a29c7a72839bb5276a7523e7e865bafd3daa2894ee434df11da9f88f9d0469f1"},"schema_version":"1.0","source":{"id":"2307.00467","kind":"arxiv","version":1}},"canonical_sha256":"b5834a0a412d004185ca9c79859eda1ce56cdc8992193f13741f372680cbda5a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b5834a0a412d004185ca9c79859eda1ce56cdc8992193f13741f372680cbda5a","first_computed_at":"2026-07-05T06:26:56.606413Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:26:56.606413Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zQWaQn6gn/vpea3ZwC8qtiU1JivypyiMuTXJuywh3hHGqrwILc4s/spYmwufeSr6MRoKPL3nc5Ldpod2crxSAg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:26:56.606900Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.00467","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:10ac4b487ccbbb8e30958a81b48c646545e83b3805152e567abcc289f637d2ce","sha256:05d61de996c35844fcc18de8e927be8c8c4be2aacce4b5a5a00cbc0118da35ae"],"state_sha256":"6a4480b4c10e8a8be84dcdd51bf7237cfcadc45ffccfb221f6d8df644a9fbc9a"}