{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:SV4FPCILECSX6IDV7JT5U6RJCG","short_pith_number":"pith:SV4FPCIL","canonical_record":{"source":{"id":"1706.02375","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-07T20:37:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"aead7ace0b19e76e0acd3ca44c6aedb9749af276849cf92d568a1e3aea3bbebb","abstract_canon_sha256":"8206347c33853a4c484324fa82672ed3881bc75e239f8f8fe6c38a4b3b60ff16"},"schema_version":"1.0"},"canonical_sha256":"957857890b20a57f2075fa67da7a291197ffd08a9b7b02d7c047290d98e47c39","source":{"kind":"arxiv","id":"1706.02375","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02375","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02375v2","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02375","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"pith_short_12","alias_value":"SV4FPCILECSX","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SV4FPCILECSX6IDV","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SV4FPCIL","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:SV4FPCILECSX6IDV7JT5U6RJCG","target":"record","payload":{"canonical_record":{"source":{"id":"1706.02375","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-07T20:37:09Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"aead7ace0b19e76e0acd3ca44c6aedb9749af276849cf92d568a1e3aea3bbebb","abstract_canon_sha256":"8206347c33853a4c484324fa82672ed3881bc75e239f8f8fe6c38a4b3b60ff16"},"schema_version":"1.0"},"canonical_sha256":"957857890b20a57f2075fa67da7a291197ffd08a9b7b02d7c047290d98e47c39","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:19.291575Z","signature_b64":"gKxPDCreqCJW/N/Sd6CjhVf/aRKAx60aLdk6MXPsBe1swc6u7tXv+Zr0rdypYhTeMA1m84p1Zd2fbxsod6rzCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"957857890b20a57f2075fa67da7a291197ffd08a9b7b02d7c047290d98e47c39","last_reissued_at":"2026-05-18T00:31:19.290874Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:19.290874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.02375","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CyqmocNQj/GPPOPM+tC7QGQ6bJR/LDYTHpqYpf83Fq17KdRxhKDTT7nrk00XQsi2roFF91/wbU1IHs5d1YhZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T02:13:54.960544Z"},"content_sha256":"482fdb9d00e3a7e21a3824f83cefffb23af4d1534ad571ea5712600851de3f92","schema_version":"1.0","event_id":"sha256:482fdb9d00e3a7e21a3824f83cefffb23af4d1534ad571ea5712600851de3f92"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:SV4FPCILECSX6IDV7JT5U6RJCG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Black-box Variational Inference through Stochastic Trust-Region Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jeffrey Regier, Jon McAuliffe, Michael I. Jordan","submitted_at":"2017-06-07T20:37:09Z","abstract_excerpt":"We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick. At each iteration, TrustVI proposes and assesses a step based on minibatches of draws from the variational distribution. The algorithm provably converges to a stationary point. We implemented TrustVI in the Stan framework and compared it to two alternatives: Automatic Differentiation Variational Inference (ADVI) and Hessian-free Stochastic Gradient Variational Inference (HFSGVI). The former is based on stochastic first-order optimization. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02375","kind":"arxiv","version":2},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1wybBcIfZ/SmkfSwB/kxjCS5ylxE9gHwL98HvoxQWRgEIm9UAt2d6mEiH8ff0f7OF/119Nny684Jj38HNE+WDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T02:13:54.960900Z"},"content_sha256":"abc49e5a074a3121d1aa56ed74d5782d1b74f73adc040d5e446161cf0d5ed19f","schema_version":"1.0","event_id":"sha256:abc49e5a074a3121d1aa56ed74d5782d1b74f73adc040d5e446161cf0d5ed19f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SV4FPCILECSX6IDV7JT5U6RJCG/bundle.json","state_url":"https://pith.science/pith/SV4FPCILECSX6IDV7JT5U6RJCG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SV4FPCILECSX6IDV7JT5U6RJCG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-03T02:13:54Z","links":{"resolver":"https://pith.science/pith/SV4FPCILECSX6IDV7JT5U6RJCG","bundle":"https://pith.science/pith/SV4FPCILECSX6IDV7JT5U6RJCG/bundle.json","state":"https://pith.science/pith/SV4FPCILECSX6IDV7JT5U6RJCG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SV4FPCILECSX6IDV7JT5U6RJCG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SV4FPCILECSX6IDV7JT5U6RJCG","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":"8206347c33853a4c484324fa82672ed3881bc75e239f8f8fe6c38a4b3b60ff16","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-07T20:37:09Z","title_canon_sha256":"aead7ace0b19e76e0acd3ca44c6aedb9749af276849cf92d568a1e3aea3bbebb"},"schema_version":"1.0","source":{"id":"1706.02375","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02375","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02375v2","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02375","created_at":"2026-05-18T00:31:19Z"},{"alias_kind":"pith_short_12","alias_value":"SV4FPCILECSX","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SV4FPCILECSX6IDV","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SV4FPCIL","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:abc49e5a074a3121d1aa56ed74d5782d1b74f73adc040d5e446161cf0d5ed19f","target":"graph","created_at":"2026-05-18T00:31:19Z","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"},"paper":{"abstract_excerpt":"We introduce TrustVI, a fast second-order algorithm for black-box variational inference based on trust-region optimization and the reparameterization trick. At each iteration, TrustVI proposes and assesses a step based on minibatches of draws from the variational distribution. The algorithm provably converges to a stationary point. We implemented TrustVI in the Stan framework and compared it to two alternatives: Automatic Differentiation Variational Inference (ADVI) and Hessian-free Stochastic Gradient Variational Inference (HFSGVI). The former is based on stochastic first-order optimization. ","authors_text":"Jeffrey Regier, Jon McAuliffe, Michael I. Jordan","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-07T20:37:09Z","title":"Fast Black-box Variational Inference through Stochastic Trust-Region Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02375","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":"sha256:482fdb9d00e3a7e21a3824f83cefffb23af4d1534ad571ea5712600851de3f92","target":"record","created_at":"2026-05-18T00:31:19Z","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":"8206347c33853a4c484324fa82672ed3881bc75e239f8f8fe6c38a4b3b60ff16","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-06-07T20:37:09Z","title_canon_sha256":"aead7ace0b19e76e0acd3ca44c6aedb9749af276849cf92d568a1e3aea3bbebb"},"schema_version":"1.0","source":{"id":"1706.02375","kind":"arxiv","version":2}},"canonical_sha256":"957857890b20a57f2075fa67da7a291197ffd08a9b7b02d7c047290d98e47c39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"957857890b20a57f2075fa67da7a291197ffd08a9b7b02d7c047290d98e47c39","first_computed_at":"2026-05-18T00:31:19.290874Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:19.290874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gKxPDCreqCJW/N/Sd6CjhVf/aRKAx60aLdk6MXPsBe1swc6u7tXv+Zr0rdypYhTeMA1m84p1Zd2fbxsod6rzCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:19.291575Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.02375","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:482fdb9d00e3a7e21a3824f83cefffb23af4d1534ad571ea5712600851de3f92","sha256:abc49e5a074a3121d1aa56ed74d5782d1b74f73adc040d5e446161cf0d5ed19f"],"state_sha256":"e7330666a1eaaee5136284be07e009802ab69cb564109ffba26db44b31773305"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dfW77D0g026DDVLnZWz8JaL5TcYJIqYIkzUGqCP+1GuJJQAsY3sYIqCOG5pwR69tr4Jz7npOjD3vcpLLXng3Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T02:13:54.962967Z","bundle_sha256":"69f16da00641d91d89c851109903a023996a1d57ab9ff3a2581213526500517c"}}