{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:HDQXFLBSS4M3HYV3F2SDTBQPOH","short_pith_number":"pith:HDQXFLBS","schema_version":"1.0","canonical_sha256":"38e172ac329719b3e2bb2ea439860f71e51f02f5401dd83da30416f836678ab7","source":{"kind":"arxiv","id":"2602.21357","version":2},"attestation_state":"computed","paper":{"title":"Conditional neural control variates for variance reduction in Bayesian inverse problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ali Siahkoohi, Hyunwoo Oh","submitted_at":"2026-02-24T20:40:20Z","abstract_excerpt":"Bayesian inference for inverse problems involves computing expectations under posterior distributions--e.g., posterior means, variances, or predictive quantities--typically via Monte Carlo (MC) estimation. When the quantity of interest varies significantly under the posterior, accurate estimates demand many samples--a cost often prohibitive for partial differential equation-constrained problems. To address this challenge, we introduce conditional neural control variates, a modular method that learns amortized control variates from joint model-data samples to reduce the variance of MC estimator"},"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":"2602.21357","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-02-24T20:40:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"db90fe774c3d543b1f8839efc1240c730083968316e4f46eb7e4a118d11781b0","abstract_canon_sha256":"c44caa617098b93a0132656708c55b7d68224fea74ec02dfe77db2f755b29f45"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T01:12:02.213811Z","signature_b64":"YZzy+6p6nGM8x9NMSG9Nif1pgxgRWpzIphrcGbbpyuNLRIQEyaTS8OtgZyS/j93FyY2rj/1RdISerNJJJqvGAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38e172ac329719b3e2bb2ea439860f71e51f02f5401dd83da30416f836678ab7","last_reissued_at":"2026-06-23T01:12:02.213322Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T01:12:02.213322Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conditional neural control variates for variance reduction in Bayesian inverse problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ali Siahkoohi, Hyunwoo Oh","submitted_at":"2026-02-24T20:40:20Z","abstract_excerpt":"Bayesian inference for inverse problems involves computing expectations under posterior distributions--e.g., posterior means, variances, or predictive quantities--typically via Monte Carlo (MC) estimation. When the quantity of interest varies significantly under the posterior, accurate estimates demand many samples--a cost often prohibitive for partial differential equation-constrained problems. To address this challenge, we introduce conditional neural control variates, a modular method that learns amortized control variates from joint model-data samples to reduce the variance of MC estimator"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.21357","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2602.21357/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":"2602.21357","created_at":"2026-06-23T01:12:02.213406+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.21357v2","created_at":"2026-06-23T01:12:02.213406+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.21357","created_at":"2026-06-23T01:12:02.213406+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDQXFLBSS4M3","created_at":"2026-06-23T01:12:02.213406+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDQXFLBSS4M3HYV3","created_at":"2026-06-23T01:12:02.213406+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDQXFLBS","created_at":"2026-06-23T01:12:02.213406+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/HDQXFLBSS4M3HYV3F2SDTBQPOH","json":"https://pith.science/pith/HDQXFLBSS4M3HYV3F2SDTBQPOH.json","graph_json":"https://pith.science/api/pith-number/HDQXFLBSS4M3HYV3F2SDTBQPOH/graph.json","events_json":"https://pith.science/api/pith-number/HDQXFLBSS4M3HYV3F2SDTBQPOH/events.json","paper":"https://pith.science/paper/HDQXFLBS"},"agent_actions":{"view_html":"https://pith.science/pith/HDQXFLBSS4M3HYV3F2SDTBQPOH","download_json":"https://pith.science/pith/HDQXFLBSS4M3HYV3F2SDTBQPOH.json","view_paper":"https://pith.science/paper/HDQXFLBS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.21357&json=true","fetch_graph":"https://pith.science/api/pith-number/HDQXFLBSS4M3HYV3F2SDTBQPOH/graph.json","fetch_events":"https://pith.science/api/pith-number/HDQXFLBSS4M3HYV3F2SDTBQPOH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDQXFLBSS4M3HYV3F2SDTBQPOH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDQXFLBSS4M3HYV3F2SDTBQPOH/action/storage_attestation","attest_author":"https://pith.science/pith/HDQXFLBSS4M3HYV3F2SDTBQPOH/action/author_attestation","sign_citation":"https://pith.science/pith/HDQXFLBSS4M3HYV3F2SDTBQPOH/action/citation_signature","submit_replication":"https://pith.science/pith/HDQXFLBSS4M3HYV3F2SDTBQPOH/action/replication_record"}},"created_at":"2026-06-23T01:12:02.213406+00:00","updated_at":"2026-06-23T01:12:02.213406+00:00"}