{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4KGXKMHU2LA563JUT3ROO6W3XA","short_pith_number":"pith:4KGXKMHU","schema_version":"1.0","canonical_sha256":"e28d7530f4d2c1df6d349ee2e77adbb81361b40391f4bf7798ef82dc6353d49d","source":{"kind":"arxiv","id":"1906.00303","version":1},"attestation_state":"computed","paper":{"title":"Active Learning for Binary Classification with Abstention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Mohammad Ghavamzadeh, Shubhanshu Shekhar, Tara Javidi","submitted_at":"2019-06-01T22:23:45Z","abstract_excerpt":"We construct and analyze active learning algorithms for the problem of binary classification with abstention. We consider three abstention settings: \\emph{fixed-cost} and two variants of \\emph{bounded-rate} abstention, and for each of them propose an active learning algorithm. All the proposed algorithms can work in the most commonly used active learning models, i.e., \\emph{membership-query}, \\emph{pool-based}, and \\emph{stream-based} sampling. We obtain upper-bounds on the excess risk of our algorithms in a general non-parametric framework and establish their minimax near-optimality by derivi"},"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":"1906.00303","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-01T22:23:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"303e7530d617b46714d14f724e8d57ac9185a6f669b863ec20d0bb73990ec380","abstract_canon_sha256":"b9ec85dda183e68139d04bce03ad03540fad5e74af199e8f71df2283a524603b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:27.512445Z","signature_b64":"OLegfXO//6DbDPRB2+ESE7F3M12+iyXPdNT/Ptt51nPfNwyNyg4odWOSaCFtMfUs0rdmmRcVo3AAKU+qoZf/CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e28d7530f4d2c1df6d349ee2e77adbb81361b40391f4bf7798ef82dc6353d49d","last_reissued_at":"2026-05-17T23:44:27.511793Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:27.511793Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Active Learning for Binary Classification with Abstention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Mohammad Ghavamzadeh, Shubhanshu Shekhar, Tara Javidi","submitted_at":"2019-06-01T22:23:45Z","abstract_excerpt":"We construct and analyze active learning algorithms for the problem of binary classification with abstention. We consider three abstention settings: \\emph{fixed-cost} and two variants of \\emph{bounded-rate} abstention, and for each of them propose an active learning algorithm. All the proposed algorithms can work in the most commonly used active learning models, i.e., \\emph{membership-query}, \\emph{pool-based}, and \\emph{stream-based} sampling. We obtain upper-bounds on the excess risk of our algorithms in a general non-parametric framework and establish their minimax near-optimality by derivi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00303","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":""},"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":"1906.00303","created_at":"2026-05-17T23:44:27.511888+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.00303v1","created_at":"2026-05-17T23:44:27.511888+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00303","created_at":"2026-05-17T23:44:27.511888+00:00"},{"alias_kind":"pith_short_12","alias_value":"4KGXKMHU2LA5","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"4KGXKMHU2LA563JU","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"4KGXKMHU","created_at":"2026-05-18T12:33:10.108867+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4KGXKMHU2LA563JUT3ROO6W3XA","json":"https://pith.science/pith/4KGXKMHU2LA563JUT3ROO6W3XA.json","graph_json":"https://pith.science/api/pith-number/4KGXKMHU2LA563JUT3ROO6W3XA/graph.json","events_json":"https://pith.science/api/pith-number/4KGXKMHU2LA563JUT3ROO6W3XA/events.json","paper":"https://pith.science/paper/4KGXKMHU"},"agent_actions":{"view_html":"https://pith.science/pith/4KGXKMHU2LA563JUT3ROO6W3XA","download_json":"https://pith.science/pith/4KGXKMHU2LA563JUT3ROO6W3XA.json","view_paper":"https://pith.science/paper/4KGXKMHU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.00303&json=true","fetch_graph":"https://pith.science/api/pith-number/4KGXKMHU2LA563JUT3ROO6W3XA/graph.json","fetch_events":"https://pith.science/api/pith-number/4KGXKMHU2LA563JUT3ROO6W3XA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4KGXKMHU2LA563JUT3ROO6W3XA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4KGXKMHU2LA563JUT3ROO6W3XA/action/storage_attestation","attest_author":"https://pith.science/pith/4KGXKMHU2LA563JUT3ROO6W3XA/action/author_attestation","sign_citation":"https://pith.science/pith/4KGXKMHU2LA563JUT3ROO6W3XA/action/citation_signature","submit_replication":"https://pith.science/pith/4KGXKMHU2LA563JUT3ROO6W3XA/action/replication_record"}},"created_at":"2026-05-17T23:44:27.511888+00:00","updated_at":"2026-05-17T23:44:27.511888+00:00"}