{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZR5GJNX65VTB277V4DHE35TRCV","short_pith_number":"pith:ZR5GJNX6","canonical_record":{"source":{"id":"2202.07549","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-15T16:33:48Z","cross_cats_sorted":["cs.AI","math.OC","stat.ML"],"title_canon_sha256":"31ab6167322068120b998261b1a23b55761abd8c0223ac23fa6019ce676f0ab8","abstract_canon_sha256":"a6d22e8388056f72a18f0cb664a193096ddb640e5475d7a564d2a7b352968019"},"schema_version":"1.0"},"canonical_sha256":"cc7a64b6feed661d7ff5e0ce4df671154bf3b8ea78003fb8d2eba61a78465599","source":{"kind":"arxiv","id":"2202.07549","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.07549","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"arxiv_version","alias_value":"2202.07549v4","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.07549","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"pith_short_12","alias_value":"ZR5GJNX65VTB","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"pith_short_16","alias_value":"ZR5GJNX65VTB277V","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"pith_short_8","alias_value":"ZR5GJNX6","created_at":"2026-07-05T04:28:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZR5GJNX65VTB277V4DHE35TRCV","target":"record","payload":{"canonical_record":{"source":{"id":"2202.07549","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-15T16:33:48Z","cross_cats_sorted":["cs.AI","math.OC","stat.ML"],"title_canon_sha256":"31ab6167322068120b998261b1a23b55761abd8c0223ac23fa6019ce676f0ab8","abstract_canon_sha256":"a6d22e8388056f72a18f0cb664a193096ddb640e5475d7a564d2a7b352968019"},"schema_version":"1.0"},"canonical_sha256":"cc7a64b6feed661d7ff5e0ce4df671154bf3b8ea78003fb8d2eba61a78465599","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:28:42.907828Z","signature_b64":"qXQa1mBS6FDUWV5X+I6Ec06pOHjG2C0htEGzAYt5Yt9WqfInzTo0q77aY2Byo19+w5/vRVDkXWc61VCeRzg0Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc7a64b6feed661d7ff5e0ce4df671154bf3b8ea78003fb8d2eba61a78465599","last_reissued_at":"2026-07-05T04:28:42.907351Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:28:42.907351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.07549","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:28:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X3dNfD8Z33sLLcCKqYxkK5ksAalDQz17HAiCi/XjkwVKDDWauZ3unSiN0Zvu/wv+XTvNp/4Y/nROWwINMO99Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T23:17:42.366319Z"},"content_sha256":"b78ab501d34928f81746b2bdca7f8f1025691646aca3466681331d59c062796c","schema_version":"1.0","event_id":"sha256:b78ab501d34928f81746b2bdca7f8f1025691646aca3466681331d59c062796c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZR5GJNX65VTB277V4DHE35TRCV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Multi-Objective Bayesian Optimization Under Input Noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Enlu Zhou, Eytan Bakshy, Maximilian Balandat, Michael A. Osborne, Sait Cakmak, Samuel Daulton","submitted_at":"2022-02-15T16:33:48Z","abstract_excerpt":"Bayesian optimization (BO) is a sample-efficient approach for tuning design parameters to optimize expensive-to-evaluate, black-box performance metrics. In many manufacturing processes, the design parameters are subject to random input noise, resulting in a product that is often less performant than expected. Although BO methods have been proposed for optimizing a single objective under input noise, no existing method addresses the practical scenario where there are multiple objectives that are sensitive to input perturbations. In this work, we propose the first multi-objective BO method that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.07549","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2202.07549/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T04:28:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pErYht0jJZQWp7v0JPoqVE31zw1NDj6UbUpezqxIXx+k9/HZ4qTn48f1WqWuFR4n+6mTrPaquXKup0550ckVBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T23:17:42.366891Z"},"content_sha256":"0ada0a121618d80bd49718949b4cd45853adcf495924001f71f3fb78f1159384","schema_version":"1.0","event_id":"sha256:0ada0a121618d80bd49718949b4cd45853adcf495924001f71f3fb78f1159384"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZR5GJNX65VTB277V4DHE35TRCV/bundle.json","state_url":"https://pith.science/pith/ZR5GJNX65VTB277V4DHE35TRCV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZR5GJNX65VTB277V4DHE35TRCV/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":"2026-08-14T23:17:42Z","links":{"resolver":"https://pith.science/pith/ZR5GJNX65VTB277V4DHE35TRCV","bundle":"https://pith.science/pith/ZR5GJNX65VTB277V4DHE35TRCV/bundle.json","state":"https://pith.science/pith/ZR5GJNX65VTB277V4DHE35TRCV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZR5GJNX65VTB277V4DHE35TRCV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZR5GJNX65VTB277V4DHE35TRCV","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":"a6d22e8388056f72a18f0cb664a193096ddb640e5475d7a564d2a7b352968019","cross_cats_sorted":["cs.AI","math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-15T16:33:48Z","title_canon_sha256":"31ab6167322068120b998261b1a23b55761abd8c0223ac23fa6019ce676f0ab8"},"schema_version":"1.0","source":{"id":"2202.07549","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.07549","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"arxiv_version","alias_value":"2202.07549v4","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.07549","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"pith_short_12","alias_value":"ZR5GJNX65VTB","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"pith_short_16","alias_value":"ZR5GJNX65VTB277V","created_at":"2026-07-05T04:28:42Z"},{"alias_kind":"pith_short_8","alias_value":"ZR5GJNX6","created_at":"2026-07-05T04:28:42Z"}],"graph_snapshots":[{"event_id":"sha256:0ada0a121618d80bd49718949b4cd45853adcf495924001f71f3fb78f1159384","target":"graph","created_at":"2026-07-05T04:28:42Z","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/2202.07549/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian optimization (BO) is a sample-efficient approach for tuning design parameters to optimize expensive-to-evaluate, black-box performance metrics. In many manufacturing processes, the design parameters are subject to random input noise, resulting in a product that is often less performant than expected. Although BO methods have been proposed for optimizing a single objective under input noise, no existing method addresses the practical scenario where there are multiple objectives that are sensitive to input perturbations. In this work, we propose the first multi-objective BO method that ","authors_text":"Enlu Zhou, Eytan Bakshy, Maximilian Balandat, Michael A. Osborne, Sait Cakmak, Samuel Daulton","cross_cats":["cs.AI","math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-15T16:33:48Z","title":"Robust Multi-Objective Bayesian Optimization Under Input Noise"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.07549","kind":"arxiv","version":4},"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:b78ab501d34928f81746b2bdca7f8f1025691646aca3466681331d59c062796c","target":"record","created_at":"2026-07-05T04:28:42Z","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":"a6d22e8388056f72a18f0cb664a193096ddb640e5475d7a564d2a7b352968019","cross_cats_sorted":["cs.AI","math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-02-15T16:33:48Z","title_canon_sha256":"31ab6167322068120b998261b1a23b55761abd8c0223ac23fa6019ce676f0ab8"},"schema_version":"1.0","source":{"id":"2202.07549","kind":"arxiv","version":4}},"canonical_sha256":"cc7a64b6feed661d7ff5e0ce4df671154bf3b8ea78003fb8d2eba61a78465599","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc7a64b6feed661d7ff5e0ce4df671154bf3b8ea78003fb8d2eba61a78465599","first_computed_at":"2026-07-05T04:28:42.907351Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:28:42.907351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qXQa1mBS6FDUWV5X+I6Ec06pOHjG2C0htEGzAYt5Yt9WqfInzTo0q77aY2Byo19+w5/vRVDkXWc61VCeRzg0Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T04:28:42.907828Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.07549","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b78ab501d34928f81746b2bdca7f8f1025691646aca3466681331d59c062796c","sha256:0ada0a121618d80bd49718949b4cd45853adcf495924001f71f3fb78f1159384"],"state_sha256":"37a2893cef9df2337564d3bc041b9cda9b5ed720ddf6d8a3273f379c350fdfbe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DK4lrJR4eHqiHooknJ1LKhgrKpQztyEVfzkdP6s5UOVXlT6Vy7LCJPJ29/2D2Ybg2//ntLmvHmyEUdeixMrABA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T23:17:42.371476Z","bundle_sha256":"37ba705f6837aac87847e7a51674e07e34ec4136b87a1cff02d6f7ef2cba650d"}}