{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3POLG3WDP77SHWJEGDYWUOE5JH","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":"7898280d7c011c059ea1969b38b511c0c9e6d1784384bd341ba853c269bfb592","cross_cats_sorted":["cs.LG","math.AP","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-02T16:20:05Z","title_canon_sha256":"25cd99b32000083e2d8aa03363d87438df43f8579c8fd4d2d5c6353cceda8921"},"schema_version":"1.0","source":{"id":"1912.00894","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.00894","created_at":"2026-07-05T05:40:41Z"},{"alias_kind":"arxiv_version","alias_value":"1912.00894v2","created_at":"2026-07-05T05:40:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.00894","created_at":"2026-07-05T05:40:41Z"},{"alias_kind":"pith_short_12","alias_value":"3POLG3WDP77S","created_at":"2026-07-05T05:40:41Z"},{"alias_kind":"pith_short_16","alias_value":"3POLG3WDP77SHWJE","created_at":"2026-07-05T05:40:41Z"},{"alias_kind":"pith_short_8","alias_value":"3POLG3WD","created_at":"2026-07-05T05:40:41Z"}],"graph_snapshots":[{"event_id":"sha256:af3b3d752cb0741cb560a419af66d031383e6438619acad60798c9ad908cc510","target":"graph","created_at":"2026-07-05T05:40:41Z","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/1912.00894/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian inference problems require sampling or approximating high-dimensional probability distributions. The focus of this paper is on the recently introduced Stein variational gradient descent methodology, a class of algorithms that rely on iterated steepest descent steps with respect to a reproducing kernel Hilbert space norm. This construction leads to interacting particle systems, the mean-field limit of which is a gradient flow on the space of probability distributions equipped with a certain geometrical structure. We leverage this viewpoint to shed some light on the convergence properti","authors_text":"A. Duncan, L. Szpruch, N. Nuesken","cross_cats":["cs.LG","math.AP","math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-02T16:20:05Z","title":"On the geometry of Stein variational gradient descent"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.00894","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":"sha256:61f6993dc55cfd8a3290b3bc8732d987c91f0a87e3450ca093a1e884b7e5c9c1","target":"record","created_at":"2026-07-05T05:40:41Z","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":"7898280d7c011c059ea1969b38b511c0c9e6d1784384bd341ba853c269bfb592","cross_cats_sorted":["cs.LG","math.AP","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-02T16:20:05Z","title_canon_sha256":"25cd99b32000083e2d8aa03363d87438df43f8579c8fd4d2d5c6353cceda8921"},"schema_version":"1.0","source":{"id":"1912.00894","kind":"arxiv","version":2}},"canonical_sha256":"dbdcb36ec37fff23d92430f16a389d49fe0f3270d362b6645aee9d7046fc94fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dbdcb36ec37fff23d92430f16a389d49fe0f3270d362b6645aee9d7046fc94fe","first_computed_at":"2026-07-05T05:40:41.197814Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:40:41.197814Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vo2vJdGaXUJvo9qDaqQD4eDK0LINKH9oa+vccZWRlzcHUlh0KCQvfBvRnsg5uo8CxAZ+aHDDBpeDU6bVnF+ACA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:40:41.198349Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.00894","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:61f6993dc55cfd8a3290b3bc8732d987c91f0a87e3450ca093a1e884b7e5c9c1","sha256:af3b3d752cb0741cb560a419af66d031383e6438619acad60798c9ad908cc510"],"state_sha256":"4373573890483f4ac3ab7d81a4b511c8a6d688edfa65567fb2030905c7948e23"}