{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:Y42X5WOVQS7TEZWGADTXTVKLZI","short_pith_number":"pith:Y42X5WOV","schema_version":"1.0","canonical_sha256":"c7357ed9d584bf3266c600e779d54bca1315729d8abdd3fba1e2b9b039fe66fd","source":{"kind":"arxiv","id":"2606.00309","version":1},"attestation_state":"computed","paper":{"title":"Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jonathan H. Huggins, Xiaoyu Wang","submitted_at":"2026-05-29T19:34:28Z","abstract_excerpt":"Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models. However, it remains unclear how to tune the algorithm's hyperparameters in a principled manner to ensure the uncertainty estimates are statistically meaningful. In this work, we address this gap in tuning guidance by developing a statistical scaling limit theory for SGLD--Gibbs. We derive a joint asymptotic limit for the global parameters and latent variables under appropriate space-time rescaling. We show that global p"},"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":"2606.00309","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T19:34:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"05e0ade3b1b26162d7c7587e1c63daf0caee2eb753813d92c5fb15ed7a12f760","abstract_canon_sha256":"7b857c1797da22b065d00c45fb831b82274c8bff4c2a5a02ca0b11ffc52effaf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:03:51.238551Z","signature_b64":"i4m9QJRQthEPFfnAYyATX5FHdayjv1nJAUmfcfK0fVEQ5gRs7Dm5cEwJlx2NwVFKkLX8pxChCxfoXPaDvzS+Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7357ed9d584bf3266c600e779d54bca1315729d8abdd3fba1e2b9b039fe66fd","last_reissued_at":"2026-06-02T01:03:51.238145Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:03:51.238145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jonathan H. Huggins, Xiaoyu Wang","submitted_at":"2026-05-29T19:34:28Z","abstract_excerpt":"Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models. However, it remains unclear how to tune the algorithm's hyperparameters in a principled manner to ensure the uncertainty estimates are statistically meaningful. In this work, we address this gap in tuning guidance by developing a statistical scaling limit theory for SGLD--Gibbs. We derive a joint asymptotic limit for the global parameters and latent variables under appropriate space-time rescaling. We show that global p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00309","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/2606.00309/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":"2606.00309","created_at":"2026-06-02T01:03:51.238197+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.00309v1","created_at":"2026-06-02T01:03:51.238197+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00309","created_at":"2026-06-02T01:03:51.238197+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y42X5WOVQS7T","created_at":"2026-06-02T01:03:51.238197+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y42X5WOVQS7TEZWG","created_at":"2026-06-02T01:03:51.238197+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y42X5WOV","created_at":"2026-06-02T01:03:51.238197+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/Y42X5WOVQS7TEZWGADTXTVKLZI","json":"https://pith.science/pith/Y42X5WOVQS7TEZWGADTXTVKLZI.json","graph_json":"https://pith.science/api/pith-number/Y42X5WOVQS7TEZWGADTXTVKLZI/graph.json","events_json":"https://pith.science/api/pith-number/Y42X5WOVQS7TEZWGADTXTVKLZI/events.json","paper":"https://pith.science/paper/Y42X5WOV"},"agent_actions":{"view_html":"https://pith.science/pith/Y42X5WOVQS7TEZWGADTXTVKLZI","download_json":"https://pith.science/pith/Y42X5WOVQS7TEZWGADTXTVKLZI.json","view_paper":"https://pith.science/paper/Y42X5WOV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.00309&json=true","fetch_graph":"https://pith.science/api/pith-number/Y42X5WOVQS7TEZWGADTXTVKLZI/graph.json","fetch_events":"https://pith.science/api/pith-number/Y42X5WOVQS7TEZWGADTXTVKLZI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y42X5WOVQS7TEZWGADTXTVKLZI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y42X5WOVQS7TEZWGADTXTVKLZI/action/storage_attestation","attest_author":"https://pith.science/pith/Y42X5WOVQS7TEZWGADTXTVKLZI/action/author_attestation","sign_citation":"https://pith.science/pith/Y42X5WOVQS7TEZWGADTXTVKLZI/action/citation_signature","submit_replication":"https://pith.science/pith/Y42X5WOVQS7TEZWGADTXTVKLZI/action/replication_record"}},"created_at":"2026-06-02T01:03:51.238197+00:00","updated_at":"2026-06-02T01:03:51.238197+00:00"}