{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MARRDQIZRR3SYPC34HSY2A7CWG","short_pith_number":"pith:MARRDQIZ","schema_version":"1.0","canonical_sha256":"602311c1198c772c3c5be1e58d03e2b195ed3f617af7fb955e1011f42af75365","source":{"kind":"arxiv","id":"2412.16416","version":1},"attestation_state":"computed","paper":{"title":"Transport Quasi-Monte Carlo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","stat.CO","stat.ML"],"primary_cat":"math.NA","authors_text":"Sifan Liu","submitted_at":"2024-12-21T00:43:12Z","abstract_excerpt":"Quasi-Monte Carlo (QMC) is a powerful method for evaluating high-dimensional integrals. However, its use is typically limited to distributions where direct sampling is straightforward, such as the uniform distribution on the unit hypercube or the Gaussian distribution. For general target distributions with potentially unnormalized densities, leveraging the low-discrepancy property of QMC to improve accuracy remains challenging. We propose training a transport map to push forward the uniform distribution on the unit hypercube to approximate the target distribution. Inspired by normalizing flows"},"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":"2412.16416","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2024-12-21T00:43:12Z","cross_cats_sorted":["cs.NA","stat.CO","stat.ML"],"title_canon_sha256":"aa74a60253499b7df232739ab35c0d88052e246b2c0725b3ddf84839e441572d","abstract_canon_sha256":"670b93a43764385d3e66078292345d3085e7340cf62adfa22ef14e6932530fc2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:45.694672Z","signature_b64":"a/F/0hPGH1Qp8BC/tB2cKeHkAh9hhUxtL8WzJvWabgO+mnvuO+KYh7b+WgvMio2YbYTY6VXEC6elYMKKQtOBDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"602311c1198c772c3c5be1e58d03e2b195ed3f617af7fb955e1011f42af75365","last_reissued_at":"2026-07-05T09:52:45.694198Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:45.694198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transport Quasi-Monte Carlo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","stat.CO","stat.ML"],"primary_cat":"math.NA","authors_text":"Sifan Liu","submitted_at":"2024-12-21T00:43:12Z","abstract_excerpt":"Quasi-Monte Carlo (QMC) is a powerful method for evaluating high-dimensional integrals. However, its use is typically limited to distributions where direct sampling is straightforward, such as the uniform distribution on the unit hypercube or the Gaussian distribution. For general target distributions with potentially unnormalized densities, leveraging the low-discrepancy property of QMC to improve accuracy remains challenging. We propose training a transport map to push forward the uniform distribution on the unit hypercube to approximate the target distribution. Inspired by normalizing flows"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16416","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/2412.16416/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":"2412.16416","created_at":"2026-07-05T09:52:45.694258+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.16416v1","created_at":"2026-07-05T09:52:45.694258+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16416","created_at":"2026-07-05T09:52:45.694258+00:00"},{"alias_kind":"pith_short_12","alias_value":"MARRDQIZRR3S","created_at":"2026-07-05T09:52:45.694258+00:00"},{"alias_kind":"pith_short_16","alias_value":"MARRDQIZRR3SYPC3","created_at":"2026-07-05T09:52:45.694258+00:00"},{"alias_kind":"pith_short_8","alias_value":"MARRDQIZ","created_at":"2026-07-05T09:52:45.694258+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/MARRDQIZRR3SYPC34HSY2A7CWG","json":"https://pith.science/pith/MARRDQIZRR3SYPC34HSY2A7CWG.json","graph_json":"https://pith.science/api/pith-number/MARRDQIZRR3SYPC34HSY2A7CWG/graph.json","events_json":"https://pith.science/api/pith-number/MARRDQIZRR3SYPC34HSY2A7CWG/events.json","paper":"https://pith.science/paper/MARRDQIZ"},"agent_actions":{"view_html":"https://pith.science/pith/MARRDQIZRR3SYPC34HSY2A7CWG","download_json":"https://pith.science/pith/MARRDQIZRR3SYPC34HSY2A7CWG.json","view_paper":"https://pith.science/paper/MARRDQIZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.16416&json=true","fetch_graph":"https://pith.science/api/pith-number/MARRDQIZRR3SYPC34HSY2A7CWG/graph.json","fetch_events":"https://pith.science/api/pith-number/MARRDQIZRR3SYPC34HSY2A7CWG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MARRDQIZRR3SYPC34HSY2A7CWG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MARRDQIZRR3SYPC34HSY2A7CWG/action/storage_attestation","attest_author":"https://pith.science/pith/MARRDQIZRR3SYPC34HSY2A7CWG/action/author_attestation","sign_citation":"https://pith.science/pith/MARRDQIZRR3SYPC34HSY2A7CWG/action/citation_signature","submit_replication":"https://pith.science/pith/MARRDQIZRR3SYPC34HSY2A7CWG/action/replication_record"}},"created_at":"2026-07-05T09:52:45.694258+00:00","updated_at":"2026-07-05T09:52:45.694258+00:00"}