{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:NQADLMUCUQMS2LINQXR6UOVXYX","short_pith_number":"pith:NQADLMUC","canonical_record":{"source":{"id":"1611.06585","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-20T20:25:39Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"8c44b60d0de15c22445ff2ffe7bfbd8fc7758d11ae70d7cafb478c4979a561ac","abstract_canon_sha256":"6582f9d483f3d709bdc877732531ed78c79838391e662007ff80ca001a3bbf87"},"schema_version":"1.0"},"canonical_sha256":"6c0035b282a4192d2d0d85e3ea3ab7c5ea5d93b482b84290af945c0ed775b3ef","source":{"kind":"arxiv","id":"1611.06585","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.06585","created_at":"2026-05-18T00:50:28Z"},{"alias_kind":"arxiv_version","alias_value":"1611.06585v2","created_at":"2026-05-18T00:50:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.06585","created_at":"2026-05-18T00:50:28Z"},{"alias_kind":"pith_short_12","alias_value":"NQADLMUCUQMS","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"NQADLMUCUQMS2LIN","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"NQADLMUC","created_at":"2026-05-18T12:30:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:NQADLMUCUQMS2LINQXR6UOVXYX","target":"record","payload":{"canonical_record":{"source":{"id":"1611.06585","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-20T20:25:39Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"8c44b60d0de15c22445ff2ffe7bfbd8fc7758d11ae70d7cafb478c4979a561ac","abstract_canon_sha256":"6582f9d483f3d709bdc877732531ed78c79838391e662007ff80ca001a3bbf87"},"schema_version":"1.0"},"canonical_sha256":"6c0035b282a4192d2d0d85e3ea3ab7c5ea5d93b482b84290af945c0ed775b3ef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:28.904488Z","signature_b64":"+c8Cu+AQDnGc3b4qXJwwNtc5+ODPvEpy1PsIPVNfW4nmEb3KChekE3HyblhtV5w+z0kVbEsk9cjgTDsGoIbpDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c0035b282a4192d2d0d85e3ea3ab7c5ea5d93b482b84290af945c0ed775b3ef","last_reissued_at":"2026-05-18T00:50:28.903815Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:28.903815Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.06585","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":"2026-05-18T00:50:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cAsxqhrB2tuokgsAhnNVfjqCrM6QHyZn391nwrSqP8e4nZso+4R0BDhwilRkjH8t72sAzZmKTHRhDl6Dy2cZAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T10:55:28.234817Z"},"content_sha256":"163b68f3f6f21ffa51d74d052a0ef952197e3a5458af979b286a7f7ed5ad8b1a","schema_version":"1.0","event_id":"sha256:163b68f3f6f21ffa51d74d052a0ef952197e3a5458af979b286a7f7ed5ad8b1a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:NQADLMUCUQMS2LINQXR6UOVXYX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variational Boosting: Iteratively Refining Posterior Approximations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Andrew C. Miller, Nicholas Foti, Ryan P. Adams","submitted_at":"2016-11-20T20:25:39Z","abstract_excerpt":"We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing the practitioner to trade computation time for accuracy. We show how to expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.06585","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":"2026-05-18T00:50:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1YNADg4E6YcbDvN3DVmpMsVR9dNygpeK4D1sLlYYQHb9zldoQGpmhu4UODoBkrQ/b0+tIzdf31HUplGQE14tCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T10:55:28.235168Z"},"content_sha256":"348916b3dd146ef4ff14fba5806a88190566a0b796f3c04f28dda777930d41a2","schema_version":"1.0","event_id":"sha256:348916b3dd146ef4ff14fba5806a88190566a0b796f3c04f28dda777930d41a2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NQADLMUCUQMS2LINQXR6UOVXYX/bundle.json","state_url":"https://pith.science/pith/NQADLMUCUQMS2LINQXR6UOVXYX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NQADLMUCUQMS2LINQXR6UOVXYX/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":"2026-07-02T10:55:28Z","links":{"resolver":"https://pith.science/pith/NQADLMUCUQMS2LINQXR6UOVXYX","bundle":"https://pith.science/pith/NQADLMUCUQMS2LINQXR6UOVXYX/bundle.json","state":"https://pith.science/pith/NQADLMUCUQMS2LINQXR6UOVXYX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NQADLMUCUQMS2LINQXR6UOVXYX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:NQADLMUCUQMS2LINQXR6UOVXYX","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":"6582f9d483f3d709bdc877732531ed78c79838391e662007ff80ca001a3bbf87","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-20T20:25:39Z","title_canon_sha256":"8c44b60d0de15c22445ff2ffe7bfbd8fc7758d11ae70d7cafb478c4979a561ac"},"schema_version":"1.0","source":{"id":"1611.06585","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.06585","created_at":"2026-05-18T00:50:28Z"},{"alias_kind":"arxiv_version","alias_value":"1611.06585v2","created_at":"2026-05-18T00:50:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.06585","created_at":"2026-05-18T00:50:28Z"},{"alias_kind":"pith_short_12","alias_value":"NQADLMUCUQMS","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_16","alias_value":"NQADLMUCUQMS2LIN","created_at":"2026-05-18T12:30:36Z"},{"alias_kind":"pith_short_8","alias_value":"NQADLMUC","created_at":"2026-05-18T12:30:36Z"}],"graph_snapshots":[{"event_id":"sha256:348916b3dd146ef4ff14fba5806a88190566a0b796f3c04f28dda777930d41a2","target":"graph","created_at":"2026-05-18T00:50:28Z","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 propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing the practitioner to trade computation time for accuracy. We show how to expand the variational approximating class by incorporating additional covariance structure and by introducing new components to form a mixture. We apply variational boosting to synthetic and real statistical models, and show that r","authors_text":"Andrew C. Miller, Nicholas Foti, Ryan P. Adams","cross_cats":["cs.LG","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-20T20:25:39Z","title":"Variational Boosting: Iteratively Refining Posterior Approximations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.06585","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":"sha256:163b68f3f6f21ffa51d74d052a0ef952197e3a5458af979b286a7f7ed5ad8b1a","target":"record","created_at":"2026-05-18T00:50:28Z","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":"6582f9d483f3d709bdc877732531ed78c79838391e662007ff80ca001a3bbf87","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-20T20:25:39Z","title_canon_sha256":"8c44b60d0de15c22445ff2ffe7bfbd8fc7758d11ae70d7cafb478c4979a561ac"},"schema_version":"1.0","source":{"id":"1611.06585","kind":"arxiv","version":2}},"canonical_sha256":"6c0035b282a4192d2d0d85e3ea3ab7c5ea5d93b482b84290af945c0ed775b3ef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c0035b282a4192d2d0d85e3ea3ab7c5ea5d93b482b84290af945c0ed775b3ef","first_computed_at":"2026-05-18T00:50:28.903815Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:50:28.903815Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+c8Cu+AQDnGc3b4qXJwwNtc5+ODPvEpy1PsIPVNfW4nmEb3KChekE3HyblhtV5w+z0kVbEsk9cjgTDsGoIbpDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:50:28.904488Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.06585","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:163b68f3f6f21ffa51d74d052a0ef952197e3a5458af979b286a7f7ed5ad8b1a","sha256:348916b3dd146ef4ff14fba5806a88190566a0b796f3c04f28dda777930d41a2"],"state_sha256":"89ec15d844791963745412f159d7bd2adede594368585c6186cac1efd7c5ea75"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DoQKkEbqXgmC2zneTLukSNNrnTa479mAk3jIWB0VB5CgAcc0WnTgwzKyprxZhXERoGB7l0atNv662/H2kfyjCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T10:55:28.237167Z","bundle_sha256":"46358e10bcec16fb5e26df68411265361afb442503f6eef0e4baba8737a75928"}}