{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4ISGOVJVN5G7OJ5XLPDAJPOKLY","short_pith_number":"pith:4ISGOVJV","schema_version":"1.0","canonical_sha256":"e2246755356f4df727b75bc604bdca5e142678d83719593f423a7c8be488b87c","source":{"kind":"arxiv","id":"2210.12122","version":1},"attestation_state":"computed","paper":{"title":"Targeted active learning for probabilistic models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Christopher Tosh, Mauricio Tec, Wesley Tansey","submitted_at":"2022-10-21T17:22:03Z","abstract_excerpt":"A fundamental task in science is to design experiments that yield valuable insights about the system under study. Mathematically, these insights can be represented as a utility or risk function that shapes the value of conducting each experiment. We present PDBAL, a targeted active learning method that adaptively designs experiments to maximize scientific utility. PDBAL takes a user-specified risk function and combines it with a probabilistic model of the experimental outcomes to choose designs that rapidly converge on a high-utility model. We prove theoretical bounds on the label complexity o"},"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":"2210.12122","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-10-21T17:22:03Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"0b0144c0d007c720c891e71fbd4cff0edb8c7a8e2ba1c05371ebede463dfdcc0","abstract_canon_sha256":"8c6f49217b706ba47e01794185d3deb8aca172c883acf4685bb6a2389dacf5f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:09.493920Z","signature_b64":"f05c2nSztyeH/h+1MeEKrgA6SsM2ZTTLODpCkeztBLUHRyNzsfMDKsX/yUFmBBG1og8FwX9si7HXvLKNsVTqAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2246755356f4df727b75bc604bdca5e142678d83719593f423a7c8be488b87c","last_reissued_at":"2026-07-05T05:09:09.493455Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:09.493455Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Targeted active learning for probabilistic models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Christopher Tosh, Mauricio Tec, Wesley Tansey","submitted_at":"2022-10-21T17:22:03Z","abstract_excerpt":"A fundamental task in science is to design experiments that yield valuable insights about the system under study. Mathematically, these insights can be represented as a utility or risk function that shapes the value of conducting each experiment. We present PDBAL, a targeted active learning method that adaptively designs experiments to maximize scientific utility. PDBAL takes a user-specified risk function and combines it with a probabilistic model of the experimental outcomes to choose designs that rapidly converge on a high-utility model. We prove theoretical bounds on the label complexity o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.12122","kind":"arxiv","version":1},"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/2210.12122/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":"2210.12122","created_at":"2026-07-05T05:09:09.493518+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.12122v1","created_at":"2026-07-05T05:09:09.493518+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.12122","created_at":"2026-07-05T05:09:09.493518+00:00"},{"alias_kind":"pith_short_12","alias_value":"4ISGOVJVN5G7","created_at":"2026-07-05T05:09:09.493518+00:00"},{"alias_kind":"pith_short_16","alias_value":"4ISGOVJVN5G7OJ5X","created_at":"2026-07-05T05:09:09.493518+00:00"},{"alias_kind":"pith_short_8","alias_value":"4ISGOVJV","created_at":"2026-07-05T05:09:09.493518+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.09829","citing_title":"Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selection","ref_index":45,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4ISGOVJVN5G7OJ5XLPDAJPOKLY","json":"https://pith.science/pith/4ISGOVJVN5G7OJ5XLPDAJPOKLY.json","graph_json":"https://pith.science/api/pith-number/4ISGOVJVN5G7OJ5XLPDAJPOKLY/graph.json","events_json":"https://pith.science/api/pith-number/4ISGOVJVN5G7OJ5XLPDAJPOKLY/events.json","paper":"https://pith.science/paper/4ISGOVJV"},"agent_actions":{"view_html":"https://pith.science/pith/4ISGOVJVN5G7OJ5XLPDAJPOKLY","download_json":"https://pith.science/pith/4ISGOVJVN5G7OJ5XLPDAJPOKLY.json","view_paper":"https://pith.science/paper/4ISGOVJV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.12122&json=true","fetch_graph":"https://pith.science/api/pith-number/4ISGOVJVN5G7OJ5XLPDAJPOKLY/graph.json","fetch_events":"https://pith.science/api/pith-number/4ISGOVJVN5G7OJ5XLPDAJPOKLY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4ISGOVJVN5G7OJ5XLPDAJPOKLY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4ISGOVJVN5G7OJ5XLPDAJPOKLY/action/storage_attestation","attest_author":"https://pith.science/pith/4ISGOVJVN5G7OJ5XLPDAJPOKLY/action/author_attestation","sign_citation":"https://pith.science/pith/4ISGOVJVN5G7OJ5XLPDAJPOKLY/action/citation_signature","submit_replication":"https://pith.science/pith/4ISGOVJVN5G7OJ5XLPDAJPOKLY/action/replication_record"}},"created_at":"2026-07-05T05:09:09.493518+00:00","updated_at":"2026-07-05T05:09:09.493518+00:00"}