{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:H4LSHUFGRLW7SVB62A22MDNM7C","short_pith_number":"pith:H4LSHUFG","schema_version":"1.0","canonical_sha256":"3f1723d0a68aedf9543ed035a60dacf8aac1be3ddd6602d0504adaf283950f13","source":{"kind":"arxiv","id":"1805.12168","version":3},"attestation_state":"computed","paper":{"title":"A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Barnab\\'as P\\'oczos, Biswajit Paria, Kirthevasan Kandasamy","submitted_at":"2018-05-30T18:27:23Z","abstract_excerpt":"Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization techniques for the multi-objective setting are pertinent when the evaluation of the functions in question are expensive. Traditional methods for multi-objective optimization, both Bayesian and otherwise, are aimed at recovering the Pareto front of these objectives. However, in certain cases a practitioner might desire to identify Pareto optimal points only in a subset of the Pareto front due to external considerations. In thi"},"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":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1805.12168","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-30T18:27:23Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"6179eed09ee581707696506bdf55e5750d71ffd12fcaeebcb01034875be193e1","abstract_canon_sha256":"fc318e599769193f1226b412116be06b6328e0201f1331577a8f2feea80104c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:48.981006Z","signature_b64":"HskvxYPucR4f2j+KT1NkYQELelc9HUvRsRXGtRVQtkyQFnj02fscL1F7+bikJwG4IxGXyh2Sl63jfUnYxYOpAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f1723d0a68aedf9543ed035a60dacf8aac1be3ddd6602d0504adaf283950f13","last_reissued_at":"2026-05-17T23:42:48.980573Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:48.980573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Barnab\\'as P\\'oczos, Biswajit Paria, Kirthevasan Kandasamy","submitted_at":"2018-05-30T18:27:23Z","abstract_excerpt":"Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization techniques for the multi-objective setting are pertinent when the evaluation of the functions in question are expensive. Traditional methods for multi-objective optimization, both Bayesian and otherwise, are aimed at recovering the Pareto front of these objectives. However, in certain cases a practitioner might desire to identify Pareto optimal points only in a subset of the Pareto front due to external considerations. In thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.12168","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":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1805.12168","created_at":"2026-05-17T23:42:48.980634+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.12168v3","created_at":"2026-05-17T23:42:48.980634+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.12168","created_at":"2026-05-17T23:42:48.980634+00:00"},{"alias_kind":"pith_short_12","alias_value":"H4LSHUFGRLW7","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"H4LSHUFGRLW7SVB6","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"H4LSHUFG","created_at":"2026-05-18T12:32:28.185984+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.03487","citing_title":"RAG-Stack: Co-Optimizing RAG Serving Performance and Quality","ref_index":55,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H4LSHUFGRLW7SVB62A22MDNM7C","json":"https://pith.science/pith/H4LSHUFGRLW7SVB62A22MDNM7C.json","graph_json":"https://pith.science/api/pith-number/H4LSHUFGRLW7SVB62A22MDNM7C/graph.json","events_json":"https://pith.science/api/pith-number/H4LSHUFGRLW7SVB62A22MDNM7C/events.json","paper":"https://pith.science/paper/H4LSHUFG"},"agent_actions":{"view_html":"https://pith.science/pith/H4LSHUFGRLW7SVB62A22MDNM7C","download_json":"https://pith.science/pith/H4LSHUFGRLW7SVB62A22MDNM7C.json","view_paper":"https://pith.science/paper/H4LSHUFG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.12168&json=true","fetch_graph":"https://pith.science/api/pith-number/H4LSHUFGRLW7SVB62A22MDNM7C/graph.json","fetch_events":"https://pith.science/api/pith-number/H4LSHUFGRLW7SVB62A22MDNM7C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H4LSHUFGRLW7SVB62A22MDNM7C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H4LSHUFGRLW7SVB62A22MDNM7C/action/storage_attestation","attest_author":"https://pith.science/pith/H4LSHUFGRLW7SVB62A22MDNM7C/action/author_attestation","sign_citation":"https://pith.science/pith/H4LSHUFGRLW7SVB62A22MDNM7C/action/citation_signature","submit_replication":"https://pith.science/pith/H4LSHUFGRLW7SVB62A22MDNM7C/action/replication_record"}},"created_at":"2026-05-17T23:42:48.980634+00:00","updated_at":"2026-05-17T23:42:48.980634+00:00"}