{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:T2CRWX3ZWMZMDZVHBCT6IOIAUM","short_pith_number":"pith:T2CRWX3Z","schema_version":"1.0","canonical_sha256":"9e851b5f79b332c1e6a708a7e43900a327bcaa25167e7138e8985f95e1e25342","source":{"kind":"arxiv","id":"1711.11216","version":1},"attestation_state":"computed","paper":{"title":"Riemannian Stein Variational Gradient Descent for Bayesian Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Chang Liu, Jun Zhu","submitted_at":"2017-11-30T04:11:46Z","abstract_excerpt":"We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian inference method that generalizes Stein Variational Gradient Descent (SVGD) to Riemann manifold. The benefits are two-folds: (i) for inference tasks in Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the unique advantages of SVGD to the Riemannian world. To appropriately transfer to Riemann manifolds, we conceive novel and non-trivial techniques for RSVGD, which are required by the intrinsically different characteris"},"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":"1711.11216","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-30T04:11:46Z","cross_cats_sorted":[],"title_canon_sha256":"41efa50fe0d2667b72deb6caebf4604f43d243c49f2317abbf6e2659e5fc343f","abstract_canon_sha256":"1d218f167f5a6277dd712a1dd01eefc4b418a459f519d68b73f9daded0b280f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:12.186748Z","signature_b64":"TjzxoN8tdpUtZsnuaFgg/5HK0uXV/4aNorFGqN+DgwTt58nElzNqT9cmWT0rljZRctKHWqRrbjLdk+xXBXrFBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e851b5f79b332c1e6a708a7e43900a327bcaa25167e7138e8985f95e1e25342","last_reissued_at":"2026-05-18T00:29:12.186093Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:12.186093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Riemannian Stein Variational Gradient Descent for Bayesian Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Chang Liu, Jun Zhu","submitted_at":"2017-11-30T04:11:46Z","abstract_excerpt":"We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian inference method that generalizes Stein Variational Gradient Descent (SVGD) to Riemann manifold. The benefits are two-folds: (i) for inference tasks in Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the unique advantages of SVGD to the Riemannian world. To appropriately transfer to Riemann manifolds, we conceive novel and non-trivial techniques for RSVGD, which are required by the intrinsically different characteris"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.11216","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":""},"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":"1711.11216","created_at":"2026-05-18T00:29:12.186184+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.11216v1","created_at":"2026-05-18T00:29:12.186184+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.11216","created_at":"2026-05-18T00:29:12.186184+00:00"},{"alias_kind":"pith_short_12","alias_value":"T2CRWX3ZWMZM","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_16","alias_value":"T2CRWX3ZWMZMDZVH","created_at":"2026-05-18T12:31:43.269735+00:00"},{"alias_kind":"pith_short_8","alias_value":"T2CRWX3Z","created_at":"2026-05-18T12:31:43.269735+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/T2CRWX3ZWMZMDZVHBCT6IOIAUM","json":"https://pith.science/pith/T2CRWX3ZWMZMDZVHBCT6IOIAUM.json","graph_json":"https://pith.science/api/pith-number/T2CRWX3ZWMZMDZVHBCT6IOIAUM/graph.json","events_json":"https://pith.science/api/pith-number/T2CRWX3ZWMZMDZVHBCT6IOIAUM/events.json","paper":"https://pith.science/paper/T2CRWX3Z"},"agent_actions":{"view_html":"https://pith.science/pith/T2CRWX3ZWMZMDZVHBCT6IOIAUM","download_json":"https://pith.science/pith/T2CRWX3ZWMZMDZVHBCT6IOIAUM.json","view_paper":"https://pith.science/paper/T2CRWX3Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.11216&json=true","fetch_graph":"https://pith.science/api/pith-number/T2CRWX3ZWMZMDZVHBCT6IOIAUM/graph.json","fetch_events":"https://pith.science/api/pith-number/T2CRWX3ZWMZMDZVHBCT6IOIAUM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T2CRWX3ZWMZMDZVHBCT6IOIAUM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T2CRWX3ZWMZMDZVHBCT6IOIAUM/action/storage_attestation","attest_author":"https://pith.science/pith/T2CRWX3ZWMZMDZVHBCT6IOIAUM/action/author_attestation","sign_citation":"https://pith.science/pith/T2CRWX3ZWMZMDZVHBCT6IOIAUM/action/citation_signature","submit_replication":"https://pith.science/pith/T2CRWX3ZWMZMDZVHBCT6IOIAUM/action/replication_record"}},"created_at":"2026-05-18T00:29:12.186184+00:00","updated_at":"2026-05-18T00:29:12.186184+00:00"}