{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:XEPXX2UIP2N3UDDTPC7CB4ZCWL","short_pith_number":"pith:XEPXX2UI","canonical_record":{"source":{"id":"2304.01111","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-04-03T16:14:31Z","cross_cats_sorted":["cs.LG","math.PR","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"813a52130d37f669fbe8d05c91187447a3e5c0031f37a4bf970753799eb74b86","abstract_canon_sha256":"98a4286d9e341d26acc489225d3d5864ecf768562b1d16b55a92003e19ab282a"},"schema_version":"1.0"},"canonical_sha256":"b91f7bea887e9bba0c7378be20f322b2c794d5860dd00bd42c20773833d1810c","source":{"kind":"arxiv","id":"2304.01111","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.01111","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"arxiv_version","alias_value":"2304.01111v2","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.01111","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"pith_short_12","alias_value":"XEPXX2UIP2N3","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"pith_short_16","alias_value":"XEPXX2UIP2N3UDDT","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"pith_short_8","alias_value":"XEPXX2UI","created_at":"2026-07-05T09:26:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:XEPXX2UIP2N3UDDTPC7CB4ZCWL","target":"record","payload":{"canonical_record":{"source":{"id":"2304.01111","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-04-03T16:14:31Z","cross_cats_sorted":["cs.LG","math.PR","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"813a52130d37f669fbe8d05c91187447a3e5c0031f37a4bf970753799eb74b86","abstract_canon_sha256":"98a4286d9e341d26acc489225d3d5864ecf768562b1d16b55a92003e19ab282a"},"schema_version":"1.0"},"canonical_sha256":"b91f7bea887e9bba0c7378be20f322b2c794d5860dd00bd42c20773833d1810c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:26:54.999443Z","signature_b64":"QU3ZFzqQvFMZSfcV767a+SHNbNe0HdH/fvP96BEHmB7TLqDxorDu6mak7Ekv96Ul/P6ZUSMM0S8GTyvGTJ+MBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b91f7bea887e9bba0c7378be20f322b2c794d5860dd00bd42c20773833d1810c","last_reissued_at":"2026-07-05T09:26:54.998872Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:26:54.998872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.01111","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:26:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D4wVhMRkrHuJDzSitnjqG2CvSPbkAL+GcnR35tlofsPGyx1DT3/bW5GvjqFGHk1CpL+fDMaHcaoCGOw28tiVAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T22:04:25.943730Z"},"content_sha256":"bf747e13cb5c896f2caa1891c3a8e4c62b413895d239eeffd482b673f0943bf9","schema_version":"1.0","event_id":"sha256:bf747e13cb5c896f2caa1891c3a8e4c62b413895d239eeffd482b673f0943bf9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:XEPXX2UIP2N3UDDTPC7CB4ZCWL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Theoretical guarantees for neural control variates in MCMC","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.PR","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Alexey Naumov, Artur Goldman, Denis Belomestny, Sergey Samsonov","submitted_at":"2023-04-03T16:14:31Z","abstract_excerpt":"In this paper, we propose a variance reduction approach for Markov chains based on additive control variates and the minimization of an appropriate estimate for the asymptotic variance. We focus on the particular case when control variates are represented as deep neural networks. We derive the optimal convergence rate of the asymptotic variance under various ergodicity assumptions on the underlying Markov chain. The proposed approach relies upon recent results on the stochastic errors of variance reduction algorithms and function approximation theory."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.01111","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/2304.01111/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:26:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NOXKGUJX3pE8fLczb6GfaC8/KdXPBIkBkoCwIQHRg1/8am6jUV1a7+hbKc0Ctnmq5oYK0IW/Ey+WdVe5tQ0cCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T22:04:25.944223Z"},"content_sha256":"4f06bbf2c3ca32f0d824a64ef6f0e59173c78c2893d62575eda45d31fd2c2954","schema_version":"1.0","event_id":"sha256:4f06bbf2c3ca32f0d824a64ef6f0e59173c78c2893d62575eda45d31fd2c2954"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XEPXX2UIP2N3UDDTPC7CB4ZCWL/bundle.json","state_url":"https://pith.science/pith/XEPXX2UIP2N3UDDTPC7CB4ZCWL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XEPXX2UIP2N3UDDTPC7CB4ZCWL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T22:04:25Z","links":{"resolver":"https://pith.science/pith/XEPXX2UIP2N3UDDTPC7CB4ZCWL","bundle":"https://pith.science/pith/XEPXX2UIP2N3UDDTPC7CB4ZCWL/bundle.json","state":"https://pith.science/pith/XEPXX2UIP2N3UDDTPC7CB4ZCWL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XEPXX2UIP2N3UDDTPC7CB4ZCWL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:XEPXX2UIP2N3UDDTPC7CB4ZCWL","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":"98a4286d9e341d26acc489225d3d5864ecf768562b1d16b55a92003e19ab282a","cross_cats_sorted":["cs.LG","math.PR","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-04-03T16:14:31Z","title_canon_sha256":"813a52130d37f669fbe8d05c91187447a3e5c0031f37a4bf970753799eb74b86"},"schema_version":"1.0","source":{"id":"2304.01111","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.01111","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"arxiv_version","alias_value":"2304.01111v2","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.01111","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"pith_short_12","alias_value":"XEPXX2UIP2N3","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"pith_short_16","alias_value":"XEPXX2UIP2N3UDDT","created_at":"2026-07-05T09:26:54Z"},{"alias_kind":"pith_short_8","alias_value":"XEPXX2UI","created_at":"2026-07-05T09:26:54Z"}],"graph_snapshots":[{"event_id":"sha256:4f06bbf2c3ca32f0d824a64ef6f0e59173c78c2893d62575eda45d31fd2c2954","target":"graph","created_at":"2026-07-05T09:26:54Z","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/2304.01111/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we propose a variance reduction approach for Markov chains based on additive control variates and the minimization of an appropriate estimate for the asymptotic variance. We focus on the particular case when control variates are represented as deep neural networks. We derive the optimal convergence rate of the asymptotic variance under various ergodicity assumptions on the underlying Markov chain. The proposed approach relies upon recent results on the stochastic errors of variance reduction algorithms and function approximation theory.","authors_text":"Alexey Naumov, Artur Goldman, Denis Belomestny, Sergey Samsonov","cross_cats":["cs.LG","math.PR","stat.ME","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-04-03T16:14:31Z","title":"Theoretical guarantees for neural control variates in MCMC"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.01111","kind":"arxiv","version":2},"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:bf747e13cb5c896f2caa1891c3a8e4c62b413895d239eeffd482b673f0943bf9","target":"record","created_at":"2026-07-05T09:26:54Z","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":"98a4286d9e341d26acc489225d3d5864ecf768562b1d16b55a92003e19ab282a","cross_cats_sorted":["cs.LG","math.PR","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-04-03T16:14:31Z","title_canon_sha256":"813a52130d37f669fbe8d05c91187447a3e5c0031f37a4bf970753799eb74b86"},"schema_version":"1.0","source":{"id":"2304.01111","kind":"arxiv","version":2}},"canonical_sha256":"b91f7bea887e9bba0c7378be20f322b2c794d5860dd00bd42c20773833d1810c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b91f7bea887e9bba0c7378be20f322b2c794d5860dd00bd42c20773833d1810c","first_computed_at":"2026-07-05T09:26:54.998872Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:26:54.998872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QU3ZFzqQvFMZSfcV767a+SHNbNe0HdH/fvP96BEHmB7TLqDxorDu6mak7Ekv96Ul/P6ZUSMM0S8GTyvGTJ+MBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:26:54.999443Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.01111","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bf747e13cb5c896f2caa1891c3a8e4c62b413895d239eeffd482b673f0943bf9","sha256:4f06bbf2c3ca32f0d824a64ef6f0e59173c78c2893d62575eda45d31fd2c2954"],"state_sha256":"cd95ff6ed292d5def990011c46dd116fe087da6d59fbaabaa8d2cc1d5a645efa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cKxewHFzo5aI6ISIWQDq2gUHGYuIzdMJ4NT1Qu//m78pRyjW+ZnEebPWJdtk8sVfllFXfYZsM4635k3RCRczAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T22:04:25.948670Z","bundle_sha256":"39980f77caa6176367790d6cc78d74438d71443ab907f5cb05fc4dcfb9fe50bc"}}