{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:LANEYJLJBHASO6PPT432GXAYRM","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":"d11b1c145e64fa7b63ca5287f81cc8595c42770cf305334362f006a4f4900503","cross_cats_sorted":["stat.ME","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T15:53:34Z","title_canon_sha256":"112c9f642b603c74fe27b8bfb2ccde4abeb7eb14cbd9048688e737d2a1bbe158"},"schema_version":"1.0","source":{"id":"2103.01085","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.01085","created_at":"2026-07-05T02:53:56Z"},{"alias_kind":"arxiv_version","alias_value":"2103.01085v2","created_at":"2026-07-05T02:53:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.01085","created_at":"2026-07-05T02:53:56Z"},{"alias_kind":"pith_short_12","alias_value":"LANEYJLJBHAS","created_at":"2026-07-05T02:53:56Z"},{"alias_kind":"pith_short_16","alias_value":"LANEYJLJBHASO6PP","created_at":"2026-07-05T02:53:56Z"},{"alias_kind":"pith_short_8","alias_value":"LANEYJLJ","created_at":"2026-07-05T02:53:56Z"}],"graph_snapshots":[{"event_id":"sha256:451a9cf7a85e156f8467e67ccecc5a3aa45eaa99d43b94a8960e6bd3349fe2ac","target":"graph","created_at":"2026-07-05T02:53:56Z","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/2103.01085/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current black-box variational inference (BBVI) methods require the user to make numerous design choices -- such as the selection of variational objective and approximating family -- yet there is little principled guidance on how to do so. We develop a conceptual framework and set of experimental tools to understand the effects of these choices, which we leverage to propose best practices for maximizing posterior approximation accuracy. Our approach is based on studying the pre-asymptotic tail behavior of the density ratios between the joint distribution and the variational approximation, then ","authors_text":"Akash Kumar Dhaka, Aki Vehtari, Alejandro Catalina, Jonathan Huggins, Manushi Welandawe, Michael Riis Andersen","cross_cats":["stat.ME","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T15:53:34Z","title":"Challenges and Opportunities in High-dimensional Variational Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.01085","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":"sha256:5412b699818e8c48234d9ec62cbc9c198a8a840d7c33bc93255f3368be78b4e4","target":"record","created_at":"2026-07-05T02:53:56Z","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":"d11b1c145e64fa7b63ca5287f81cc8595c42770cf305334362f006a4f4900503","cross_cats_sorted":["stat.ME","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-03-01T15:53:34Z","title_canon_sha256":"112c9f642b603c74fe27b8bfb2ccde4abeb7eb14cbd9048688e737d2a1bbe158"},"schema_version":"1.0","source":{"id":"2103.01085","kind":"arxiv","version":2}},"canonical_sha256":"581a4c256909c12779ef9f37a35c188b212c6edd9e37d4eca8efc85cb6d91399","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"581a4c256909c12779ef9f37a35c188b212c6edd9e37d4eca8efc85cb6d91399","first_computed_at":"2026-07-05T02:53:56.105127Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:53:56.105127Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X5zhZg++zGuUMEj18Zjzd0hNITsuAiMTtE79YKmniHG2r5yDj3EYrsAWuxBcvWpOI5Hfs2Dij3Q1ZJRGMfhNBg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:53:56.105543Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.01085","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5412b699818e8c48234d9ec62cbc9c198a8a840d7c33bc93255f3368be78b4e4","sha256:451a9cf7a85e156f8467e67ccecc5a3aa45eaa99d43b94a8960e6bd3349fe2ac"],"state_sha256":"6c4a816d9e9d217a68adfe6667c2a0569c70cc9b16ebd2529eb9292fa366f67d"}