{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DNOZSYPHGTJGXVKPR2FPLNHILL","short_pith_number":"pith:DNOZSYPH","schema_version":"1.0","canonical_sha256":"1b5d9961e734d26bd54f8e8af5b4e85ac553f0458d57912b8c4d52f1f4494ed4","source":{"kind":"arxiv","id":"2306.00096","version":2},"attestation_state":"computed","paper":{"title":"Learning the Pareto Front Using Bootstrapped Observation Samples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Assaf Zeevi, Garud Iyengar, Wonyoung Kim","submitted_at":"2023-05-31T18:15:09Z","abstract_excerpt":"We consider Pareto front identification (PFI) for linear bandits (PFILin), i.e., the goal is to identify a set of arms with undominated mean reward vectors when the mean reward vector is a linear function of the context. PFILin includes the best arm identification problem and multi-objective active learning as special cases. The sample complexity of our proposed algorithm is optimal up to a logarithmic factor. In addition, the regret incurred by our algorithm during the estimation is within a logarithmic factor of the optimal regret among all algorithms that identify the Pareto front. Our key "},"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":"2306.00096","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-05-31T18:15:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d14e230b68347b6fe6f7e57e7a64421657fde6f390b7e5819f5337df372c4a7c","abstract_canon_sha256":"2c3db75e2a0b1b30ac60d564ff00cf7dca09a2a1fe0a9424fb62d38fea12bcbc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:21:39.199354Z","signature_b64":"6uC2IeSPOhJIvwpSBTRFPsTi3/csUemWzfq3smsyN82yk/iSaqsN/zNIER7IeAJclLZRL8WVqJ2grrqFJBQNDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b5d9961e734d26bd54f8e8af5b4e85ac553f0458d57912b8c4d52f1f4494ed4","last_reissued_at":"2026-07-05T08:21:39.198874Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:21:39.198874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning the Pareto Front Using Bootstrapped Observation Samples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Assaf Zeevi, Garud Iyengar, Wonyoung Kim","submitted_at":"2023-05-31T18:15:09Z","abstract_excerpt":"We consider Pareto front identification (PFI) for linear bandits (PFILin), i.e., the goal is to identify a set of arms with undominated mean reward vectors when the mean reward vector is a linear function of the context. PFILin includes the best arm identification problem and multi-objective active learning as special cases. The sample complexity of our proposed algorithm is optimal up to a logarithmic factor. In addition, the regret incurred by our algorithm during the estimation is within a logarithmic factor of the optimal regret among all algorithms that identify the Pareto front. Our key "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.00096","kind":"arxiv","version":2},"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/2306.00096/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2306.00096","created_at":"2026-07-05T08:21:39.198928+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.00096v2","created_at":"2026-07-05T08:21:39.198928+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.00096","created_at":"2026-07-05T08:21:39.198928+00:00"},{"alias_kind":"pith_short_12","alias_value":"DNOZSYPHGTJG","created_at":"2026-07-05T08:21:39.198928+00:00"},{"alias_kind":"pith_short_16","alias_value":"DNOZSYPHGTJGXVKP","created_at":"2026-07-05T08:21:39.198928+00:00"},{"alias_kind":"pith_short_8","alias_value":"DNOZSYPH","created_at":"2026-07-05T08:21:39.198928+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.14479","citing_title":"Adaptive Data Augmentation for Thompson Sampling","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DNOZSYPHGTJGXVKPR2FPLNHILL","json":"https://pith.science/pith/DNOZSYPHGTJGXVKPR2FPLNHILL.json","graph_json":"https://pith.science/api/pith-number/DNOZSYPHGTJGXVKPR2FPLNHILL/graph.json","events_json":"https://pith.science/api/pith-number/DNOZSYPHGTJGXVKPR2FPLNHILL/events.json","paper":"https://pith.science/paper/DNOZSYPH"},"agent_actions":{"view_html":"https://pith.science/pith/DNOZSYPHGTJGXVKPR2FPLNHILL","download_json":"https://pith.science/pith/DNOZSYPHGTJGXVKPR2FPLNHILL.json","view_paper":"https://pith.science/paper/DNOZSYPH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.00096&json=true","fetch_graph":"https://pith.science/api/pith-number/DNOZSYPHGTJGXVKPR2FPLNHILL/graph.json","fetch_events":"https://pith.science/api/pith-number/DNOZSYPHGTJGXVKPR2FPLNHILL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DNOZSYPHGTJGXVKPR2FPLNHILL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DNOZSYPHGTJGXVKPR2FPLNHILL/action/storage_attestation","attest_author":"https://pith.science/pith/DNOZSYPHGTJGXVKPR2FPLNHILL/action/author_attestation","sign_citation":"https://pith.science/pith/DNOZSYPHGTJGXVKPR2FPLNHILL/action/citation_signature","submit_replication":"https://pith.science/pith/DNOZSYPHGTJGXVKPR2FPLNHILL/action/replication_record"}},"created_at":"2026-07-05T08:21:39.198928+00:00","updated_at":"2026-07-05T08:21:39.198928+00:00"}