{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:6C5XSMQGLW6RSGRI6QRIAA7HRG","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":"760eb6f940890af6ab5f462de731b98dba21e4392e6ef67fa54600f85798bf01","cross_cats_sorted":["cs.AI","cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-09T06:57:47Z","title_canon_sha256":"6aa186935d7c234990ba11dc4776c3ed422d74daf0cd34dd0e22c101fbf8d4b7"},"schema_version":"1.0","source":{"id":"2006.05078","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.05078","created_at":"2026-07-05T01:50:47Z"},{"alias_kind":"arxiv_version","alias_value":"2006.05078v3","created_at":"2026-07-05T01:50:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.05078","created_at":"2026-07-05T01:50:47Z"},{"alias_kind":"pith_short_12","alias_value":"6C5XSMQGLW6R","created_at":"2026-07-05T01:50:47Z"},{"alias_kind":"pith_short_16","alias_value":"6C5XSMQGLW6RSGRI","created_at":"2026-07-05T01:50:47Z"},{"alias_kind":"pith_short_8","alias_value":"6C5XSMQG","created_at":"2026-07-05T01:50:47Z"}],"graph_snapshots":[{"event_id":"sha256:2eb9d32e60805ff6b6703614cb7e5b73bb80a853df95ab4c7da7a2eafb792f1d","target":"graph","created_at":"2026-07-05T01:50:47Z","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/2006.05078/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization (BO) is a common approach, but many of the best-performing acquisition functions do not have known analytic gradients and suffer from high computational overhead. We leverage recent advances in programming models and hardware acceleration for multi-objective BO using Expected Hypervolume Improvement (EHVI)---an algorithm notorious for its high computational complexity. We derive a novel formulation of q-Expected Hypervolum","authors_text":"Eytan Bakshy, Maximilian Balandat, Samuel Daulton","cross_cats":["cs.AI","cs.LG","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-09T06:57:47Z","title":"Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.05078","kind":"arxiv","version":3},"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:c616a3c2c1acb7eb6e2ce3ccdc96ac3b8d4f17fbd8621a9c114c49c832602e90","target":"record","created_at":"2026-07-05T01:50:47Z","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":"760eb6f940890af6ab5f462de731b98dba21e4392e6ef67fa54600f85798bf01","cross_cats_sorted":["cs.AI","cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-09T06:57:47Z","title_canon_sha256":"6aa186935d7c234990ba11dc4776c3ed422d74daf0cd34dd0e22c101fbf8d4b7"},"schema_version":"1.0","source":{"id":"2006.05078","kind":"arxiv","version":3}},"canonical_sha256":"f0bb7932065dbd191a28f4228003e789878bbd3282b9e7d5f73ab6b246cc331b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f0bb7932065dbd191a28f4228003e789878bbd3282b9e7d5f73ab6b246cc331b","first_computed_at":"2026-07-05T01:50:47.644448Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:50:47.644448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5X3EgSm1F0tJmzSoBQu7J2eG8SlM16Dhlp2Y6u5LmNleAEk1nTfZ5PRyLwokcEgEIHXzKH3MtHrKCyZotdd+DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:50:47.644931Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.05078","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c616a3c2c1acb7eb6e2ce3ccdc96ac3b8d4f17fbd8621a9c114c49c832602e90","sha256:2eb9d32e60805ff6b6703614cb7e5b73bb80a853df95ab4c7da7a2eafb792f1d"],"state_sha256":"60f2e91ced4d0b3e8d0d08ccb95c6da9f928be622d05ea5fdf7e764ed0c8f1a1"}