{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:2CGEU4VVZYB65PWXAOWYIMJBYF","short_pith_number":"pith:2CGEU4VV","schema_version":"1.0","canonical_sha256":"d08c4a72b5ce03eebed703ad843121c149f8e33c218c79d7d770a61fed7d75ec","source":{"kind":"arxiv","id":"1312.5021","version":1},"attestation_state":"computed","paper":{"title":"Efficient Online Bootstrapping for Large Scale Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"John Langford, Lihong Li, Nikos Karampatziakis, Vaclav Petricek, Zhen Qin","submitted_at":"2013-12-18T02:10:21Z","abstract_excerpt":"Bootstrapping is a useful technique for estimating the uncertainty of a predictor, for example, confidence intervals for prediction. It is typically used on small to moderate sized datasets, due to its high computation cost. This work describes a highly scalable online bootstrapping strategy, implemented inside Vowpal Wabbit, that is several times faster than traditional strategies. Our experiments indicate that, in addition to providing a black box-like method for estimating uncertainty, our implementation of online bootstrapping may also help to train models with better prediction performanc"},"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":"1312.5021","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2013-12-18T02:10:21Z","cross_cats_sorted":[],"title_canon_sha256":"ba7ddb3f24d624f7a66d0cb7e72c3c1aa7c7040326a6d5eefcab6a90f441d568","abstract_canon_sha256":"2ed55e98e32c9a3977ce923f53a17d060095ea241e77c69db2fb8dd575d2c727"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:04:16.538732Z","signature_b64":"fNG98ABUeFuKciR3idds1UYS3Jp3/2i/XcXlemsk3s5n/UAb8mig7A8aP7hXY1nC1xHupjV7tBzG8vLWB9KTBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d08c4a72b5ce03eebed703ad843121c149f8e33c218c79d7d770a61fed7d75ec","last_reissued_at":"2026-05-18T03:04:16.538275Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:04:16.538275Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Online Bootstrapping for Large Scale Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"John Langford, Lihong Li, Nikos Karampatziakis, Vaclav Petricek, Zhen Qin","submitted_at":"2013-12-18T02:10:21Z","abstract_excerpt":"Bootstrapping is a useful technique for estimating the uncertainty of a predictor, for example, confidence intervals for prediction. It is typically used on small to moderate sized datasets, due to its high computation cost. This work describes a highly scalable online bootstrapping strategy, implemented inside Vowpal Wabbit, that is several times faster than traditional strategies. Our experiments indicate that, in addition to providing a black box-like method for estimating uncertainty, our implementation of online bootstrapping may also help to train models with better prediction performanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.5021","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":"1312.5021","created_at":"2026-05-18T03:04:16.538345+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.5021v1","created_at":"2026-05-18T03:04:16.538345+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.5021","created_at":"2026-05-18T03:04:16.538345+00:00"},{"alias_kind":"pith_short_12","alias_value":"2CGEU4VVZYB6","created_at":"2026-05-18T12:27:30.460161+00:00"},{"alias_kind":"pith_short_16","alias_value":"2CGEU4VVZYB65PWX","created_at":"2026-05-18T12:27:30.460161+00:00"},{"alias_kind":"pith_short_8","alias_value":"2CGEU4VV","created_at":"2026-05-18T12:27:30.460161+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.10717","citing_title":"Uncertainty-aware Model-based Policy Optimization","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2CGEU4VVZYB65PWXAOWYIMJBYF","json":"https://pith.science/pith/2CGEU4VVZYB65PWXAOWYIMJBYF.json","graph_json":"https://pith.science/api/pith-number/2CGEU4VVZYB65PWXAOWYIMJBYF/graph.json","events_json":"https://pith.science/api/pith-number/2CGEU4VVZYB65PWXAOWYIMJBYF/events.json","paper":"https://pith.science/paper/2CGEU4VV"},"agent_actions":{"view_html":"https://pith.science/pith/2CGEU4VVZYB65PWXAOWYIMJBYF","download_json":"https://pith.science/pith/2CGEU4VVZYB65PWXAOWYIMJBYF.json","view_paper":"https://pith.science/paper/2CGEU4VV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.5021&json=true","fetch_graph":"https://pith.science/api/pith-number/2CGEU4VVZYB65PWXAOWYIMJBYF/graph.json","fetch_events":"https://pith.science/api/pith-number/2CGEU4VVZYB65PWXAOWYIMJBYF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2CGEU4VVZYB65PWXAOWYIMJBYF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2CGEU4VVZYB65PWXAOWYIMJBYF/action/storage_attestation","attest_author":"https://pith.science/pith/2CGEU4VVZYB65PWXAOWYIMJBYF/action/author_attestation","sign_citation":"https://pith.science/pith/2CGEU4VVZYB65PWXAOWYIMJBYF/action/citation_signature","submit_replication":"https://pith.science/pith/2CGEU4VVZYB65PWXAOWYIMJBYF/action/replication_record"}},"created_at":"2026-05-18T03:04:16.538345+00:00","updated_at":"2026-05-18T03:04:16.538345+00:00"}