{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:HGXIBRY2SKMK22BFQPVUINYPT4","short_pith_number":"pith:HGXIBRY2","schema_version":"1.0","canonical_sha256":"39ae80c71a9298ad682583eb44370f9f2275ab7f55800d7bdbb539e84b86c8ce","source":{"kind":"arxiv","id":"1606.08549","version":1},"attestation_state":"computed","paper":{"title":"Automatic Variational ABC","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Alexander Moreno, Edward Meeds, James M. Rehg, Max Welling, Tameem Adel","submitted_at":"2016-06-28T04:16:25Z","abstract_excerpt":"Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appealing alternative to the inefficient sampling approaches commonly used in ABC. However, SVI is highly sensitive to the variance of the gradient estimators, and this problem is exacerbated by approximating the likelihood. We draw upon recent advances in variance reduction for SV and likelihood-free inference using deterministic simulations to produce low variance gradient estimators of the variational lower-bound. By the"},"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":"1606.08549","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-06-28T04:16:25Z","cross_cats_sorted":[],"title_canon_sha256":"805eb378e4c6cd06af65a3731f55a5b4fc02919eefb7f9f1abccec1a1cf5c8d1","abstract_canon_sha256":"ffccac675a9ca88f30d58e118a429a69d1783a0a9b738de13194b98e4dc5909b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:11:47.462207Z","signature_b64":"qOtgh3y9+CDbFRU2KZxAAZwjX8v/Yj3Wc31ETEGiKQWLtvFsgRbS5Inn0LEuydiVOr/nyQx1HYFaLml+I+SuBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39ae80c71a9298ad682583eb44370f9f2275ab7f55800d7bdbb539e84b86c8ce","last_reissued_at":"2026-05-18T01:11:47.461848Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:11:47.461848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Variational ABC","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Alexander Moreno, Edward Meeds, James M. Rehg, Max Welling, Tameem Adel","submitted_at":"2016-06-28T04:16:25Z","abstract_excerpt":"Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appealing alternative to the inefficient sampling approaches commonly used in ABC. However, SVI is highly sensitive to the variance of the gradient estimators, and this problem is exacerbated by approximating the likelihood. We draw upon recent advances in variance reduction for SV and likelihood-free inference using deterministic simulations to produce low variance gradient estimators of the variational lower-bound. By the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.08549","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":"1606.08549","created_at":"2026-05-18T01:11:47.461905+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.08549v1","created_at":"2026-05-18T01:11:47.461905+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.08549","created_at":"2026-05-18T01:11:47.461905+00:00"},{"alias_kind":"pith_short_12","alias_value":"HGXIBRY2SKMK","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"HGXIBRY2SKMK22BF","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"HGXIBRY2","created_at":"2026-05-18T12:30:19.053100+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/HGXIBRY2SKMK22BFQPVUINYPT4","json":"https://pith.science/pith/HGXIBRY2SKMK22BFQPVUINYPT4.json","graph_json":"https://pith.science/api/pith-number/HGXIBRY2SKMK22BFQPVUINYPT4/graph.json","events_json":"https://pith.science/api/pith-number/HGXIBRY2SKMK22BFQPVUINYPT4/events.json","paper":"https://pith.science/paper/HGXIBRY2"},"agent_actions":{"view_html":"https://pith.science/pith/HGXIBRY2SKMK22BFQPVUINYPT4","download_json":"https://pith.science/pith/HGXIBRY2SKMK22BFQPVUINYPT4.json","view_paper":"https://pith.science/paper/HGXIBRY2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.08549&json=true","fetch_graph":"https://pith.science/api/pith-number/HGXIBRY2SKMK22BFQPVUINYPT4/graph.json","fetch_events":"https://pith.science/api/pith-number/HGXIBRY2SKMK22BFQPVUINYPT4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HGXIBRY2SKMK22BFQPVUINYPT4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HGXIBRY2SKMK22BFQPVUINYPT4/action/storage_attestation","attest_author":"https://pith.science/pith/HGXIBRY2SKMK22BFQPVUINYPT4/action/author_attestation","sign_citation":"https://pith.science/pith/HGXIBRY2SKMK22BFQPVUINYPT4/action/citation_signature","submit_replication":"https://pith.science/pith/HGXIBRY2SKMK22BFQPVUINYPT4/action/replication_record"}},"created_at":"2026-05-18T01:11:47.461905+00:00","updated_at":"2026-05-18T01:11:47.461905+00:00"}