{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:I7QY5PJDZEZ5UFMFLLDIUWDE3P","short_pith_number":"pith:I7QY5PJD","canonical_record":{"source":{"id":"2306.04262","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-07T09:00:19Z","cross_cats_sorted":[],"title_canon_sha256":"c6919b4afec5de5ecdb6cc312d3eb5bda80fb2b2a9d56e8d6a76be9392c63697","abstract_canon_sha256":"4058c85d3ccdabf0b8e269286949896bed28c038cbc78505417c5a59b238b683"},"schema_version":"1.0"},"canonical_sha256":"47e18ebd23c933da15855ac68a5864dbe5e0954639cd7c5f6e40b849ac9effd4","source":{"kind":"arxiv","id":"2306.04262","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.04262","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"arxiv_version","alias_value":"2306.04262v3","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.04262","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"pith_short_12","alias_value":"I7QY5PJDZEZ5","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"pith_short_16","alias_value":"I7QY5PJDZEZ5UFMF","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"pith_short_8","alias_value":"I7QY5PJD","created_at":"2026-07-05T06:26:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:I7QY5PJDZEZ5UFMFLLDIUWDE3P","target":"record","payload":{"canonical_record":{"source":{"id":"2306.04262","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-07T09:00:19Z","cross_cats_sorted":[],"title_canon_sha256":"c6919b4afec5de5ecdb6cc312d3eb5bda80fb2b2a9d56e8d6a76be9392c63697","abstract_canon_sha256":"4058c85d3ccdabf0b8e269286949896bed28c038cbc78505417c5a59b238b683"},"schema_version":"1.0"},"canonical_sha256":"47e18ebd23c933da15855ac68a5864dbe5e0954639cd7c5f6e40b849ac9effd4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:21.812942Z","signature_b64":"0iAP3uvrFwk4yAtFHXOxSZeP/HR6zvgcjdgVNEJJrxcRHsTHg29e19/+XPSVdbcdWpgEV5v3P7Yr0UOUBkoFBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47e18ebd23c933da15855ac68a5864dbe5e0954639cd7c5f6e40b849ac9effd4","last_reissued_at":"2026-07-05T06:26:21.812442Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:21.812442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.04262","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:26:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ia6R2Csxq8x20sygG/QdBTDg86nwTB/vsfUlvwOZFZp+Z6STrAbx9G+BTwgicU4jJUKh9SVyly2p5INy6OS2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T06:37:23.827045Z"},"content_sha256":"c219f117c64b928a0cf8185fbf5c1800e2718f16dd1353678f7b134eb4ccbefe","schema_version":"1.0","event_id":"sha256:c219f117c64b928a0cf8185fbf5c1800e2718f16dd1353678f7b134eb4ccbefe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:I7QY5PJDZEZ5UFMFLLDIUWDE3P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Adjusting Weighted Expected Improvement for Bayesian Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Anja Jankovic, Carola Doerr, Carolin Benjamins, Elena Raponi, Marius Lindauer","submitted_at":"2023-06-07T09:00:19Z","abstract_excerpt":"Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is highly configurable with many different design choices regarding the initial design, surrogate model, and acquisition function (AF). Unfortunately, our understanding of how to select suitable components for a problem at hand is very limited. In this work, we focus on the definition of the AF, whose main purpose is to balance the trade-off between exploring regions with high uncertainty and those with high promise for go"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.04262","kind":"arxiv","version":3},"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/2306.04262/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":"2026-07-05T06:26:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tAoXySgDA1sLjZBuzJGxDK7ykBWqE50gEMUToBOdN+FG8vQIn72YX0T0yWPeIAa/WmikHcnCfdfuCTo3El+bBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-20T06:37:23.827420Z"},"content_sha256":"c40b6ade87a1a8db1fb448e59a101b6afe8f864f796e18585707d6c2fbefe0c7","schema_version":"1.0","event_id":"sha256:c40b6ade87a1a8db1fb448e59a101b6afe8f864f796e18585707d6c2fbefe0c7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I7QY5PJDZEZ5UFMFLLDIUWDE3P/bundle.json","state_url":"https://pith.science/pith/I7QY5PJDZEZ5UFMFLLDIUWDE3P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I7QY5PJDZEZ5UFMFLLDIUWDE3P/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":"2026-07-20T06:37:23Z","links":{"resolver":"https://pith.science/pith/I7QY5PJDZEZ5UFMFLLDIUWDE3P","bundle":"https://pith.science/pith/I7QY5PJDZEZ5UFMFLLDIUWDE3P/bundle.json","state":"https://pith.science/pith/I7QY5PJDZEZ5UFMFLLDIUWDE3P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I7QY5PJDZEZ5UFMFLLDIUWDE3P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:I7QY5PJDZEZ5UFMFLLDIUWDE3P","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":"4058c85d3ccdabf0b8e269286949896bed28c038cbc78505417c5a59b238b683","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-07T09:00:19Z","title_canon_sha256":"c6919b4afec5de5ecdb6cc312d3eb5bda80fb2b2a9d56e8d6a76be9392c63697"},"schema_version":"1.0","source":{"id":"2306.04262","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.04262","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"arxiv_version","alias_value":"2306.04262v3","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.04262","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"pith_short_12","alias_value":"I7QY5PJDZEZ5","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"pith_short_16","alias_value":"I7QY5PJDZEZ5UFMF","created_at":"2026-07-05T06:26:21Z"},{"alias_kind":"pith_short_8","alias_value":"I7QY5PJD","created_at":"2026-07-05T06:26:21Z"}],"graph_snapshots":[{"event_id":"sha256:c40b6ade87a1a8db1fb448e59a101b6afe8f864f796e18585707d6c2fbefe0c7","target":"graph","created_at":"2026-07-05T06:26:21Z","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/2306.04262/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is highly configurable with many different design choices regarding the initial design, surrogate model, and acquisition function (AF). Unfortunately, our understanding of how to select suitable components for a problem at hand is very limited. In this work, we focus on the definition of the AF, whose main purpose is to balance the trade-off between exploring regions with high uncertainty and those with high promise for go","authors_text":"Anja Jankovic, Carola Doerr, Carolin Benjamins, Elena Raponi, Marius Lindauer","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-07T09:00:19Z","title":"Self-Adjusting Weighted Expected Improvement for Bayesian Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.04262","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:c219f117c64b928a0cf8185fbf5c1800e2718f16dd1353678f7b134eb4ccbefe","target":"record","created_at":"2026-07-05T06:26:21Z","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":"4058c85d3ccdabf0b8e269286949896bed28c038cbc78505417c5a59b238b683","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-07T09:00:19Z","title_canon_sha256":"c6919b4afec5de5ecdb6cc312d3eb5bda80fb2b2a9d56e8d6a76be9392c63697"},"schema_version":"1.0","source":{"id":"2306.04262","kind":"arxiv","version":3}},"canonical_sha256":"47e18ebd23c933da15855ac68a5864dbe5e0954639cd7c5f6e40b849ac9effd4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"47e18ebd23c933da15855ac68a5864dbe5e0954639cd7c5f6e40b849ac9effd4","first_computed_at":"2026-07-05T06:26:21.812442Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:26:21.812442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0iAP3uvrFwk4yAtFHXOxSZeP/HR6zvgcjdgVNEJJrxcRHsTHg29e19/+XPSVdbcdWpgEV5v3P7Yr0UOUBkoFBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:26:21.812942Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.04262","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c219f117c64b928a0cf8185fbf5c1800e2718f16dd1353678f7b134eb4ccbefe","sha256:c40b6ade87a1a8db1fb448e59a101b6afe8f864f796e18585707d6c2fbefe0c7"],"state_sha256":"f97732e32fade243d10c0352a4416de0a2b8f96df7e698d0ce4714bbfc000215"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XwMz18sNkMGd9Y1CevKbHld9VyfUZAyElwcH7RQcq0DzoVWmmw7PRr4c76OAGLwYKQaqtWg5QO37hoX4T6WoCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-20T06:37:23.829791Z","bundle_sha256":"06015b51eaa4244dd6bd475245bb1ef1ab981708ac04f25272ea2e4aa8417f47"}}