{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:H6QVPE65VZ2T7NMD66L66NAADS","short_pith_number":"pith:H6QVPE65","schema_version":"1.0","canonical_sha256":"3fa15793ddae753fb583f797ef34001cb34ff02f60a19b634a243ad084a7cdc3","source":{"kind":"arxiv","id":"1810.11693","version":1},"attestation_state":"computed","paper":{"title":"Stein Variational Gradient Descent as Moment Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dilin Wang, Qiang Liu","submitted_at":"2018-10-27T19:23:50Z","abstract_excerpt":"Stein variational gradient descent (SVGD) is a non-parametric inference algorithm that evolves a set of particles to fit a given distribution of interest. We analyze the non-asymptotic properties of SVGD, showing that there exists a set of functions, which we call the Stein matching set, whose expectations are exactly estimated by any set of particles that satisfies the fixed point equation of SVGD. This set is the image of Stein operator applied on the feature maps of the positive definite kernel used in SVGD. Our results provide a theoretical framework for analyzing the properties of SVGD wi"},"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":"1810.11693","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-27T19:23:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"65c183cda2496a36095512b291c30bc7b415d092df3fb96f49c09f05f0c0d2f1","abstract_canon_sha256":"f274a0317d9eb16a720cf3a54b9af49a689edef53cf7d6de358fd6e374c26e36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:07.738302Z","signature_b64":"NjFIAzPsQWVhei8k09HXs7jiMOH/TjIRHCrG0mzfWocbmQbwxtEf5ivcjf+ED87v/Bsm83b7F4NUcfNvIAEzDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fa15793ddae753fb583f797ef34001cb34ff02f60a19b634a243ad084a7cdc3","last_reissued_at":"2026-05-18T00:02:07.737637Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:07.737637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stein Variational Gradient Descent as Moment Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dilin Wang, Qiang Liu","submitted_at":"2018-10-27T19:23:50Z","abstract_excerpt":"Stein variational gradient descent (SVGD) is a non-parametric inference algorithm that evolves a set of particles to fit a given distribution of interest. We analyze the non-asymptotic properties of SVGD, showing that there exists a set of functions, which we call the Stein matching set, whose expectations are exactly estimated by any set of particles that satisfies the fixed point equation of SVGD. This set is the image of Stein operator applied on the feature maps of the positive definite kernel used in SVGD. Our results provide a theoretical framework for analyzing the properties of SVGD wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11693","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":"1810.11693","created_at":"2026-05-18T00:02:07.737736+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.11693v1","created_at":"2026-05-18T00:02:07.737736+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.11693","created_at":"2026-05-18T00:02:07.737736+00:00"},{"alias_kind":"pith_short_12","alias_value":"H6QVPE65VZ2T","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"H6QVPE65VZ2T7NMD","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"H6QVPE65","created_at":"2026-05-18T12:32:28.185984+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/H6QVPE65VZ2T7NMD66L66NAADS","json":"https://pith.science/pith/H6QVPE65VZ2T7NMD66L66NAADS.json","graph_json":"https://pith.science/api/pith-number/H6QVPE65VZ2T7NMD66L66NAADS/graph.json","events_json":"https://pith.science/api/pith-number/H6QVPE65VZ2T7NMD66L66NAADS/events.json","paper":"https://pith.science/paper/H6QVPE65"},"agent_actions":{"view_html":"https://pith.science/pith/H6QVPE65VZ2T7NMD66L66NAADS","download_json":"https://pith.science/pith/H6QVPE65VZ2T7NMD66L66NAADS.json","view_paper":"https://pith.science/paper/H6QVPE65","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.11693&json=true","fetch_graph":"https://pith.science/api/pith-number/H6QVPE65VZ2T7NMD66L66NAADS/graph.json","fetch_events":"https://pith.science/api/pith-number/H6QVPE65VZ2T7NMD66L66NAADS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H6QVPE65VZ2T7NMD66L66NAADS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H6QVPE65VZ2T7NMD66L66NAADS/action/storage_attestation","attest_author":"https://pith.science/pith/H6QVPE65VZ2T7NMD66L66NAADS/action/author_attestation","sign_citation":"https://pith.science/pith/H6QVPE65VZ2T7NMD66L66NAADS/action/citation_signature","submit_replication":"https://pith.science/pith/H6QVPE65VZ2T7NMD66L66NAADS/action/replication_record"}},"created_at":"2026-05-18T00:02:07.737736+00:00","updated_at":"2026-05-18T00:02:07.737736+00:00"}