{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FSUSJWZ5UWQGIDP33ULVZ3MB3O","short_pith_number":"pith:FSUSJWZ5","schema_version":"1.0","canonical_sha256":"2ca924db3da5a0640dfbdd175ced81dbb394732a82427d2788cee54c2d4d1cf5","source":{"kind":"arxiv","id":"2502.16870","version":3},"attestation_state":"computed","paper":{"title":"Distributionally Robust Active Learning for Gaussian Process Regression","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hanju Lee, Hiroyuki Hanada, Ichiro Takeuchi, Noriaki Hashimoto, Satoshi Akahane, Shinya Kojima, Shion Takeno, Taro Murayama, Tatsuya Aoyama, Tomonari Tanaka, Yoshito Okura, Yu Inatsu","submitted_at":"2025-02-24T06:14:27Z","abstract_excerpt":"Gaussian process regression (GPR) or kernel ridge regression is a widely used and powerful tool for nonlinear prediction. Therefore, active learning (AL) for GPR, which actively collects data labels to achieve an accurate prediction with fewer data labels, is an important problem. However, existing AL methods do not theoretically guarantee prediction accuracy for target distribution. Furthermore, as discussed in the distributionally robust learning literature, specifying the target distribution is often difficult. Thus, this paper proposes two AL methods that effectively reduce the worst-case "},"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":"2502.16870","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-24T06:14:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"98c7e3bca880277dcd7912328e819b064c1ef3bb312b1a11de15a042cbb6e166","abstract_canon_sha256":"6246cfd3d7fcbbc6ca50a64e0e35a3d90f59cb15532a06c39f8bc688e0d71716"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:32:25.308726Z","signature_b64":"U9MOS95KCMHN8SzNdaHtDQFHLM3utimBp2GAn4LdRVf6b+amqUud4v3UdZZxAnmFFz1ZrZBOZLcV+gEklggfDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ca924db3da5a0640dfbdd175ced81dbb394732a82427d2788cee54c2d4d1cf5","last_reissued_at":"2026-07-05T11:32:25.308194Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:32:25.308194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributionally Robust Active Learning for Gaussian Process Regression","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hanju Lee, Hiroyuki Hanada, Ichiro Takeuchi, Noriaki Hashimoto, Satoshi Akahane, Shinya Kojima, Shion Takeno, Taro Murayama, Tatsuya Aoyama, Tomonari Tanaka, Yoshito Okura, Yu Inatsu","submitted_at":"2025-02-24T06:14:27Z","abstract_excerpt":"Gaussian process regression (GPR) or kernel ridge regression is a widely used and powerful tool for nonlinear prediction. Therefore, active learning (AL) for GPR, which actively collects data labels to achieve an accurate prediction with fewer data labels, is an important problem. However, existing AL methods do not theoretically guarantee prediction accuracy for target distribution. Furthermore, as discussed in the distributionally robust learning literature, specifying the target distribution is often difficult. Thus, this paper proposes two AL methods that effectively reduce the worst-case "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.16870","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/2502.16870/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":"2502.16870","created_at":"2026-07-05T11:32:25.308254+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.16870v3","created_at":"2026-07-05T11:32:25.308254+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.16870","created_at":"2026-07-05T11:32:25.308254+00:00"},{"alias_kind":"pith_short_12","alias_value":"FSUSJWZ5UWQG","created_at":"2026-07-05T11:32:25.308254+00:00"},{"alias_kind":"pith_short_16","alias_value":"FSUSJWZ5UWQGIDP3","created_at":"2026-07-05T11:32:25.308254+00:00"},{"alias_kind":"pith_short_8","alias_value":"FSUSJWZ5","created_at":"2026-07-05T11:32:25.308254+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10654","citing_title":"Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FSUSJWZ5UWQGIDP33ULVZ3MB3O","json":"https://pith.science/pith/FSUSJWZ5UWQGIDP33ULVZ3MB3O.json","graph_json":"https://pith.science/api/pith-number/FSUSJWZ5UWQGIDP33ULVZ3MB3O/graph.json","events_json":"https://pith.science/api/pith-number/FSUSJWZ5UWQGIDP33ULVZ3MB3O/events.json","paper":"https://pith.science/paper/FSUSJWZ5"},"agent_actions":{"view_html":"https://pith.science/pith/FSUSJWZ5UWQGIDP33ULVZ3MB3O","download_json":"https://pith.science/pith/FSUSJWZ5UWQGIDP33ULVZ3MB3O.json","view_paper":"https://pith.science/paper/FSUSJWZ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.16870&json=true","fetch_graph":"https://pith.science/api/pith-number/FSUSJWZ5UWQGIDP33ULVZ3MB3O/graph.json","fetch_events":"https://pith.science/api/pith-number/FSUSJWZ5UWQGIDP33ULVZ3MB3O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FSUSJWZ5UWQGIDP33ULVZ3MB3O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FSUSJWZ5UWQGIDP33ULVZ3MB3O/action/storage_attestation","attest_author":"https://pith.science/pith/FSUSJWZ5UWQGIDP33ULVZ3MB3O/action/author_attestation","sign_citation":"https://pith.science/pith/FSUSJWZ5UWQGIDP33ULVZ3MB3O/action/citation_signature","submit_replication":"https://pith.science/pith/FSUSJWZ5UWQGIDP33ULVZ3MB3O/action/replication_record"}},"created_at":"2026-07-05T11:32:25.308254+00:00","updated_at":"2026-07-05T11:32:25.308254+00:00"}