{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:ZM5C63MYZFQGF3DHNVDQEOBYPA","short_pith_number":"pith:ZM5C63MY","schema_version":"1.0","canonical_sha256":"cb3a2f6d98c96062ec676d47023838781893ac4dc38abf54e0b7619d60836e6d","source":{"kind":"arxiv","id":"1309.6830","version":1},"attestation_state":"computed","paper":{"title":"Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander G. Gray, Ravi Ganti","submitted_at":"2013-09-26T12:39:01Z","abstract_excerpt":"In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi-armed bandit literature to design our proposed algorithm. Our algorithm is a sequential algorithm, which in each round assigns a sampling distribution on the pool, samples one point from this distribution, and queries the oracle for the label of this sampled point. The design of"},"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":"1309.6830","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-09-26T12:39:01Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"fe0466e949996ecb29813d8a2a7d2325713327374627ed1dbbc9339a69186963","abstract_canon_sha256":"fc3357677f11539c7c97c40c8cd9505ca5146274c2af70faa71af21307656788"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:12:10.257346Z","signature_b64":"l9ncv9n5cUTJnjqHON5QggH7WUdcN+SKqHEP111ORNqKbOnag5RNitTQaFHdvX5eN5xDqFhhJdqdpNwL20llCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb3a2f6d98c96062ec676d47023838781893ac4dc38abf54e0b7619d60836e6d","last_reissued_at":"2026-05-18T03:12:10.256119Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:12:10.256119Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander G. Gray, Ravi Ganti","submitted_at":"2013-09-26T12:39:01Z","abstract_excerpt":"In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi-armed bandit literature to design our proposed algorithm. Our algorithm is a sequential algorithm, which in each round assigns a sampling distribution on the pool, samples one point from this distribution, and queries the oracle for the label of this sampled point. The design of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.6830","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":"1309.6830","created_at":"2026-05-18T03:12:10.256574+00:00"},{"alias_kind":"arxiv_version","alias_value":"1309.6830v1","created_at":"2026-05-18T03:12:10.256574+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1309.6830","created_at":"2026-05-18T03:12:10.256574+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZM5C63MYZFQG","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZM5C63MYZFQGF3DH","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZM5C63MY","created_at":"2026-05-18T12:28:09.283467+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/ZM5C63MYZFQGF3DHNVDQEOBYPA","json":"https://pith.science/pith/ZM5C63MYZFQGF3DHNVDQEOBYPA.json","graph_json":"https://pith.science/api/pith-number/ZM5C63MYZFQGF3DHNVDQEOBYPA/graph.json","events_json":"https://pith.science/api/pith-number/ZM5C63MYZFQGF3DHNVDQEOBYPA/events.json","paper":"https://pith.science/paper/ZM5C63MY"},"agent_actions":{"view_html":"https://pith.science/pith/ZM5C63MYZFQGF3DHNVDQEOBYPA","download_json":"https://pith.science/pith/ZM5C63MYZFQGF3DHNVDQEOBYPA.json","view_paper":"https://pith.science/paper/ZM5C63MY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1309.6830&json=true","fetch_graph":"https://pith.science/api/pith-number/ZM5C63MYZFQGF3DHNVDQEOBYPA/graph.json","fetch_events":"https://pith.science/api/pith-number/ZM5C63MYZFQGF3DHNVDQEOBYPA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZM5C63MYZFQGF3DHNVDQEOBYPA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZM5C63MYZFQGF3DHNVDQEOBYPA/action/storage_attestation","attest_author":"https://pith.science/pith/ZM5C63MYZFQGF3DHNVDQEOBYPA/action/author_attestation","sign_citation":"https://pith.science/pith/ZM5C63MYZFQGF3DHNVDQEOBYPA/action/citation_signature","submit_replication":"https://pith.science/pith/ZM5C63MYZFQGF3DHNVDQEOBYPA/action/replication_record"}},"created_at":"2026-05-18T03:12:10.256574+00:00","updated_at":"2026-05-18T03:12:10.256574+00:00"}