{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:Y2TLRLELJAS7P6FWPCQWHASXR7","short_pith_number":"pith:Y2TLRLEL","schema_version":"1.0","canonical_sha256":"c6a6b8ac8b4825f7f8b678a16382578ff4fccc0b5ad990dd263275c7f2e8405b","source":{"kind":"arxiv","id":"1807.02811","version":1},"attestation_state":"computed","paper":{"title":"A Tutorial on Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Bayesian optimization builds a Gaussian process surrogate for an expensive objective and uses an acquisition function to choose each next evaluation point.","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Peter I. Frazier","submitted_at":"2018-07-08T13:06:26Z","abstract_excerpt":"Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian machine learning technique, Gaussian process regression, and then uses an acquisition function defined from this surrogate to decide where to sample. In this tutorial, we describe how Bayesian optimization works, including Gau"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":true,"formal_links_present":true},"canonical_record":{"source":{"id":"1807.02811","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-08T13:06:26Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"9159c215c228d4f2f4a272fa1e1e27fe7c4db68eea975dd95b9864614368fcf9","abstract_canon_sha256":"85f042f5376ce92a90d01f095623e3ff4dec0a068769ff7841ba8e1b771d9436"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T22:53:53.807916Z","signature_b64":"mcaIlyD2qm6IaM/VoCPUJrWISOc/7cZ7fdorVrHorfPz70imxYqYaoKfIDg6BqbckeRULzGowOX3G30bzgZxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6a6b8ac8b4825f7f8b678a16382578ff4fccc0b5ad990dd263275c7f2e8405b","last_reissued_at":"2026-07-04T22:53:53.807374Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T22:53:53.807374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Tutorial on Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Bayesian optimization builds a Gaussian process surrogate for an expensive objective and uses an acquisition function to choose each next evaluation point.","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Peter I. Frazier","submitted_at":"2018-07-08T13:06:26Z","abstract_excerpt":"Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian machine learning technique, Gaussian process regression, and then uses an acquisition function defined from this surrogate to decide where to sample. In this tutorial, we describe how Bayesian optimization works, including Gau"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We provide a generalization of expected improvement to noisy evaluations, justified by a formal decision-theoretic argument, standing in contrast to previous ad hoc modifications.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The tutorial assumes that a Gaussian process provides an adequate surrogate model for the objective function and that the reader has sufficient background in Bayesian methods and Gaussian processes.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Bayesian optimization builds a Gaussian process surrogate for an expensive objective and uses an acquisition function to choose each next evaluation point.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"56842bd2672bf678e2d85168199b6b51317b4b5a4c770ca0dd44e980c6c9e688"},"source":{"id":"1807.02811","kind":"arxiv","version":1},"verdict":{"id":"d2611f24-f700-4b86-a532-32295201701d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T14:12:39.323428Z","strongest_claim":"We provide a generalization of expected improvement to noisy evaluations, justified by a formal decision-theoretic argument, standing in contrast to previous ad hoc modifications.","one_line_summary":"Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The tutorial assumes that a Gaussian process provides an adequate surrogate model for the objective function and that the reader has sufficient background in Bayesian methods and Gaussian processes.","pith_extraction_headline":"Bayesian optimization builds a Gaussian process surrogate for an expensive objective and uses an acquisition function to choose each next evaluation point."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1807.02811/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":101,"sample":[{"doi":"","year":2016,"title":"O., Shahriari, B., and Schmidt, M","work_id":"cc143d0e-dfbe-402e-a2bb-9b27b6591cc3","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2013,"title":"Berger, J. O. (2013). Statistical Decision Theory and Bayesian Analysis . Springer Science & Business Media","work_id":"4c6e7a1b-f6ad-4c90-bb19-e7aac5c6e815","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1954,"title":"Blum, J. R. (1954). Multidimensional stochastic approximation methods. The Annals of Mathematical Statistics , pages 737--744","work_id":"d83fc444-3a13-4ef9-b289-cabae357f8ba","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1999,"title":"Booker, A., Dennis, J., Frank, P., Serafini, D., Torczon, V., and Trosset, M. (1999). A rigorous framework for optimization of expensive functions by surrogates . Structural and Multidisciplinary Opti","work_id":"c86e3966-3e0d-475a-ac15-c5c25232bb89","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2012,"title":"Bottou, L. (2012). Stochastic gradient descent tricks. In Montavon, G., Orr, G. B., and M \\\"u ller, K. R., editors, Neural Networks: Tricks of the Trade , pages 421--436. Springer","work_id":"35c07c29-944d-45a0-9b53-7a1edb7d2279","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":101,"snapshot_sha256":"256b78b9b1377205ef2a4d85f1856f0727bb958298e1abd58660bb5f4d0e5ad2","internal_anchors":4},"formal_canon":{"evidence_count":2,"snapshot_sha256":"64fac19c9faaa72c0dce660d8c954e0a2855b9cca1f57e2c8b06d95b9acd30c7"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1807.02811","created_at":"2026-07-04T22:53:53.807456+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.02811v1","created_at":"2026-07-04T22:53:53.807456+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02811","created_at":"2026-07-04T22:53:53.807456+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y2TLRLELJAS7","created_at":"2026-07-04T22:53:53.807456+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y2TLRLELJAS7P6FW","created_at":"2026-07-04T22:53:53.807456+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y2TLRLEL","created_at":"2026-07-04T22:53:53.807456+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":81,"internal_anchor_count":81,"sample":[{"citing_arxiv_id":"2607.07289","citing_title":"Bayesian Optimization of Genetic Algorithm Hyperparameters in a Multi-Fidelity Framework for Efficient Lattice Material Design","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2606.25957","citing_title":"From Rubble Simulation to Active Magnetic Mapping: Quantum Sensing for Disaster Response","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2606.25301","citing_title":"Active Learning for Optimal Experimental Design in Machine Learning-Based Building Energy System Identification","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2606.24552","citing_title":"Enabling Robust Cloth Manipulation via Inference-Time Simulator-in-the-Loop Refinement","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2606.23188","citing_title":"Stage-dependent integer-binary encoding in factorization-machine black-box optimization","ref_index":28,"is_internal_anchor":true},{"citing_arxiv_id":"2606.22425","citing_title":"SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2606.19252","citing_title":"Multi-objective Bayesian optimization of rigid and flexible nozzles for energy-efficient pulsed jet propulsion","ref_index":17,"is_internal_anchor":true},{"citing_arxiv_id":"2606.18620","citing_title":"BCL: Bayesian In-Context Learning Framework for Information Extraction","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2606.12718","citing_title":"Out-of-Distribution (OOD) Detectors for Open-Set RF Fingerprinting","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2606.11247","citing_title":"Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction","ref_index":39,"is_internal_anchor":true},{"citing_arxiv_id":"2606.08438","citing_title":"Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2606.06555","citing_title":"Depth over Fidelity in Fixed-Budget Noisy Evolution Strategies","ref_index":53,"is_internal_anchor":true},{"citing_arxiv_id":"2607.00284","citing_title":"Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2606.04314","citing_title":"Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"2606.02507","citing_title":"Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2606.01730","citing_title":"Evidence-Gated LLM Priors for Multi-Objective Bayesian Optimization","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2606.01457","citing_title":"Transferring Information Across Interventions in Causal Bayesian Optimization","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2605.30841","citing_title":"BUP-TR: Bayesian Underdetermined Projection Trust-Region Methods for Derivative-Free Optimization","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2605.02405","citing_title":"Closed-Loop CO2 Storage Control With History-Based Reinforcement Learning and Latent Model-Based Adaptation","ref_index":26,"is_internal_anchor":true},{"citing_arxiv_id":"2605.20763","citing_title":"ShapeBench: A Scalable Benchmark and Diagnostic Suite for Standardized Evaluation in Aerodynamic Shape Optimization","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2606.02606","citing_title":"ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2605.24331","citing_title":"CurveRL: Principled Distribution-Aware Context Reweighting for LLM Reasoning","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2605.26741","citing_title":"MatFormBench: A Benchmarking Evaluation Framework for Target-Driven Materials Formulation","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"2605.28353","citing_title":"Improving Evaluation of Recombination-based Cartesian Genetic Programming","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2606.01226","citing_title":"Realizing leakage elimination operator-based adiabatic speedup on a superconducting quantum processor","ref_index":47,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7","json":"https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7.json","graph_json":"https://pith.science/api/pith-number/Y2TLRLELJAS7P6FWPCQWHASXR7/graph.json","events_json":"https://pith.science/api/pith-number/Y2TLRLELJAS7P6FWPCQWHASXR7/events.json","paper":"https://pith.science/paper/Y2TLRLEL"},"agent_actions":{"view_html":"https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7","download_json":"https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7.json","view_paper":"https://pith.science/paper/Y2TLRLEL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.02811&json=true","fetch_graph":"https://pith.science/api/pith-number/Y2TLRLELJAS7P6FWPCQWHASXR7/graph.json","fetch_events":"https://pith.science/api/pith-number/Y2TLRLELJAS7P6FWPCQWHASXR7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7/action/storage_attestation","attest_author":"https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7/action/author_attestation","sign_citation":"https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7/action/citation_signature","submit_replication":"https://pith.science/pith/Y2TLRLELJAS7P6FWPCQWHASXR7/action/replication_record"}},"created_at":"2026-07-04T22:53:53.807456+00:00","updated_at":"2026-07-04T22:53:53.807456+00:00"}