{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RHZFZJ3LG2UOWOFN3IKY4IJB2I","short_pith_number":"pith:RHZFZJ3L","schema_version":"1.0","canonical_sha256":"89f25ca76b36a8eb38adda158e2121d215a2c89bc57c32d4f90908d4486a53e4","source":{"kind":"arxiv","id":"2312.03307","version":1},"attestation_state":"computed","paper":{"title":"Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jong-June Jeon, Seunghwan An, Sungchul Hong","submitted_at":"2023-12-06T06:15:48Z","abstract_excerpt":"In the process of training a generative model, it becomes essential to measure the discrepancy between two high-dimensional probability distributions: the generative distribution and the ground-truth distribution of the observed dataset. Recently, there has been growing interest in an approach that involves slicing high-dimensional distributions, with the Cramer-Wold distance emerging as a promising method. However, we have identified that the Cramer-Wold distance primarily focuses on joint distributional learning, whereas understanding marginal distributional patterns is crucial for effective"},"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":"2312.03307","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2023-12-06T06:15:48Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5f7fbbbd6a2f2b951fd9c32ae5130f11eefdd638cb6640fc5d4c722339a50e1e","abstract_canon_sha256":"77dfae417625a2098d11be83f24b4aa1bf8988e5a3cb1bc47e54b40393e26428"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:21:01.797451Z","signature_b64":"TMtLMvmWv3G9QCmnQevDZii1K1kVnSS2McRXU3GIWIqPlzHZCubp2ePo4GKc//rPc76tjJgVDiS6gW7bjeDJCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89f25ca76b36a8eb38adda158e2121d215a2c89bc57c32d4f90908d4486a53e4","last_reissued_at":"2026-07-05T07:21:01.796877Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:21:01.796877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jong-June Jeon, Seunghwan An, Sungchul Hong","submitted_at":"2023-12-06T06:15:48Z","abstract_excerpt":"In the process of training a generative model, it becomes essential to measure the discrepancy between two high-dimensional probability distributions: the generative distribution and the ground-truth distribution of the observed dataset. Recently, there has been growing interest in an approach that involves slicing high-dimensional distributions, with the Cramer-Wold distance emerging as a promising method. However, we have identified that the Cramer-Wold distance primarily focuses on joint distributional learning, whereas understanding marginal distributional patterns is crucial for effective"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.03307","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.03307/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2312.03307","created_at":"2026-07-05T07:21:01.796950+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.03307v1","created_at":"2026-07-05T07:21:01.796950+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.03307","created_at":"2026-07-05T07:21:01.796950+00:00"},{"alias_kind":"pith_short_12","alias_value":"RHZFZJ3LG2UO","created_at":"2026-07-05T07:21:01.796950+00:00"},{"alias_kind":"pith_short_16","alias_value":"RHZFZJ3LG2UOWOFN","created_at":"2026-07-05T07:21:01.796950+00:00"},{"alias_kind":"pith_short_8","alias_value":"RHZFZJ3L","created_at":"2026-07-05T07:21:01.796950+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/RHZFZJ3LG2UOWOFN3IKY4IJB2I","json":"https://pith.science/pith/RHZFZJ3LG2UOWOFN3IKY4IJB2I.json","graph_json":"https://pith.science/api/pith-number/RHZFZJ3LG2UOWOFN3IKY4IJB2I/graph.json","events_json":"https://pith.science/api/pith-number/RHZFZJ3LG2UOWOFN3IKY4IJB2I/events.json","paper":"https://pith.science/paper/RHZFZJ3L"},"agent_actions":{"view_html":"https://pith.science/pith/RHZFZJ3LG2UOWOFN3IKY4IJB2I","download_json":"https://pith.science/pith/RHZFZJ3LG2UOWOFN3IKY4IJB2I.json","view_paper":"https://pith.science/paper/RHZFZJ3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.03307&json=true","fetch_graph":"https://pith.science/api/pith-number/RHZFZJ3LG2UOWOFN3IKY4IJB2I/graph.json","fetch_events":"https://pith.science/api/pith-number/RHZFZJ3LG2UOWOFN3IKY4IJB2I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RHZFZJ3LG2UOWOFN3IKY4IJB2I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RHZFZJ3LG2UOWOFN3IKY4IJB2I/action/storage_attestation","attest_author":"https://pith.science/pith/RHZFZJ3LG2UOWOFN3IKY4IJB2I/action/author_attestation","sign_citation":"https://pith.science/pith/RHZFZJ3LG2UOWOFN3IKY4IJB2I/action/citation_signature","submit_replication":"https://pith.science/pith/RHZFZJ3LG2UOWOFN3IKY4IJB2I/action/replication_record"}},"created_at":"2026-07-05T07:21:01.796950+00:00","updated_at":"2026-07-05T07:21:01.796950+00:00"}