{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:AIJMUW4IQOXYSQCSUQ4F5XWJYN","short_pith_number":"pith:AIJMUW4I","canonical_record":{"source":{"id":"2105.10347","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-21T13:39:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"473bb1c3e8adc29b317ceea97229d8e9ec686d05af99d888ec33d672485a7d18","abstract_canon_sha256":"3a39d7c94a4656677b9b27df93be72853e39055c81f4a58208165f46f22d844e"},"schema_version":"1.0"},"canonical_sha256":"0212ca5b8883af894052a4385edec9c373958b6466d9b95a7017c04dca692822","source":{"kind":"arxiv","id":"2105.10347","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.10347","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"arxiv_version","alias_value":"2105.10347v4","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.10347","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"pith_short_12","alias_value":"AIJMUW4IQOXY","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"pith_short_16","alias_value":"AIJMUW4IQOXYSQCS","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"pith_short_8","alias_value":"AIJMUW4I","created_at":"2026-07-05T05:47:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:AIJMUW4IQOXYSQCSUQ4F5XWJYN","target":"record","payload":{"canonical_record":{"source":{"id":"2105.10347","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-21T13:39:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"473bb1c3e8adc29b317ceea97229d8e9ec686d05af99d888ec33d672485a7d18","abstract_canon_sha256":"3a39d7c94a4656677b9b27df93be72853e39055c81f4a58208165f46f22d844e"},"schema_version":"1.0"},"canonical_sha256":"0212ca5b8883af894052a4385edec9c373958b6466d9b95a7017c04dca692822","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:13.791078Z","signature_b64":"remviD2NqXvSkFnk8DQe/zk5pwViFrL9wXGtRU7EUfsQImt2wm/n/laZ14qxAZaQtwnymKKDT2FZpJNhkKPuAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0212ca5b8883af894052a4385edec9c373958b6466d9b95a7017c04dca692822","last_reissued_at":"2026-07-05T05:47:13.790651Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:13.790651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.10347","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:47:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MuXhIDCnZpa7pbys9QDGC0aHIe7qabhOit/xC+5VSFA4lcdStvtn1yriBmM2TUybGRtkBZ22JBjelsftFdpFDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:39:48.290984Z"},"content_sha256":"adad063541a2f69ffb2dc1ef5e454b2841a5573f49aef4c660f4f691a119d5b2","schema_version":"1.0","event_id":"sha256:adad063541a2f69ffb2dc1ef5e454b2841a5573f49aef4c660f4f691a119d5b2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:AIJMUW4IQOXYSQCSUQ4F5XWJYN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Gabriel Stoltz, Inass Sekkat","submitted_at":"2021-05-21T13:39:39Z","abstract_excerpt":"Bayesian inference allows to obtain useful information on the parameters of models, either in computational statistics or more recently in the context of Bayesian Neural Networks. The computational cost of usual Monte Carlo methods for sampling posterior laws in Bayesian inference scales linearly with the number of data points. One option to reduce it to a fraction of this cost is to resort to mini-batching in conjunction with unadjusted discretizations of Langevin dynamics, in which case only a random fraction of the data is used to estimate the gradient. However, this leads to an additional "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.10347","kind":"arxiv","version":4},"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/2105.10347/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":"2026-07-05T05:47:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uFwIMfThJYMa+i7C8sbA2FvXka1WYrXhUstD6y3Spd2EfCortQsyFcVEkGgJdHLeW/EiF/4i/ZZu3LUXnhhvBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:39:48.291537Z"},"content_sha256":"2e03058790492b2ab1100f2dd85103c4c08d7afee1d3e5d5b8ce917c802f95b5","schema_version":"1.0","event_id":"sha256:2e03058790492b2ab1100f2dd85103c4c08d7afee1d3e5d5b8ce917c802f95b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AIJMUW4IQOXYSQCSUQ4F5XWJYN/bundle.json","state_url":"https://pith.science/pith/AIJMUW4IQOXYSQCSUQ4F5XWJYN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AIJMUW4IQOXYSQCSUQ4F5XWJYN/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":"2026-08-08T19:39:48Z","links":{"resolver":"https://pith.science/pith/AIJMUW4IQOXYSQCSUQ4F5XWJYN","bundle":"https://pith.science/pith/AIJMUW4IQOXYSQCSUQ4F5XWJYN/bundle.json","state":"https://pith.science/pith/AIJMUW4IQOXYSQCSUQ4F5XWJYN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AIJMUW4IQOXYSQCSUQ4F5XWJYN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:AIJMUW4IQOXYSQCSUQ4F5XWJYN","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":"3a39d7c94a4656677b9b27df93be72853e39055c81f4a58208165f46f22d844e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-21T13:39:39Z","title_canon_sha256":"473bb1c3e8adc29b317ceea97229d8e9ec686d05af99d888ec33d672485a7d18"},"schema_version":"1.0","source":{"id":"2105.10347","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.10347","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"arxiv_version","alias_value":"2105.10347v4","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.10347","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"pith_short_12","alias_value":"AIJMUW4IQOXY","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"pith_short_16","alias_value":"AIJMUW4IQOXYSQCS","created_at":"2026-07-05T05:47:13Z"},{"alias_kind":"pith_short_8","alias_value":"AIJMUW4I","created_at":"2026-07-05T05:47:13Z"}],"graph_snapshots":[{"event_id":"sha256:2e03058790492b2ab1100f2dd85103c4c08d7afee1d3e5d5b8ce917c802f95b5","target":"graph","created_at":"2026-07-05T05:47:13Z","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/2105.10347/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian inference allows to obtain useful information on the parameters of models, either in computational statistics or more recently in the context of Bayesian Neural Networks. The computational cost of usual Monte Carlo methods for sampling posterior laws in Bayesian inference scales linearly with the number of data points. One option to reduce it to a fraction of this cost is to resort to mini-batching in conjunction with unadjusted discretizations of Langevin dynamics, in which case only a random fraction of the data is used to estimate the gradient. However, this leads to an additional ","authors_text":"Gabriel Stoltz, Inass Sekkat","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-21T13:39:39Z","title":"Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.10347","kind":"arxiv","version":4},"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:adad063541a2f69ffb2dc1ef5e454b2841a5573f49aef4c660f4f691a119d5b2","target":"record","created_at":"2026-07-05T05:47:13Z","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":"3a39d7c94a4656677b9b27df93be72853e39055c81f4a58208165f46f22d844e","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-05-21T13:39:39Z","title_canon_sha256":"473bb1c3e8adc29b317ceea97229d8e9ec686d05af99d888ec33d672485a7d18"},"schema_version":"1.0","source":{"id":"2105.10347","kind":"arxiv","version":4}},"canonical_sha256":"0212ca5b8883af894052a4385edec9c373958b6466d9b95a7017c04dca692822","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0212ca5b8883af894052a4385edec9c373958b6466d9b95a7017c04dca692822","first_computed_at":"2026-07-05T05:47:13.790651Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:47:13.790651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"remviD2NqXvSkFnk8DQe/zk5pwViFrL9wXGtRU7EUfsQImt2wm/n/laZ14qxAZaQtwnymKKDT2FZpJNhkKPuAA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:47:13.791078Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.10347","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:adad063541a2f69ffb2dc1ef5e454b2841a5573f49aef4c660f4f691a119d5b2","sha256:2e03058790492b2ab1100f2dd85103c4c08d7afee1d3e5d5b8ce917c802f95b5"],"state_sha256":"daa134723dc363c494f5ff68755bc9bfefe06d25de8654c83586bfa3763069dd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8L4RbAj6idRUaH09CXF8DEselKg0Pj9twVQuHtEReQz69m9cunjQ8l4lq9IWAwnz9RKTaWm/gF400xA5pXeRDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T19:39:48.296376Z","bundle_sha256":"5b775d36ce1d28995cd1430f1acb0d7c1c351b03cd3dabe3f065a10e0dca7b0c"}}