{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:RN2T653AKC7LU4PBX3Z276YD34","short_pith_number":"pith:RN2T653A","schema_version":"1.0","canonical_sha256":"8b753f776050beba71e1bef3affb03df1dc93ca18cbf9cff1b87bb5d94a0c33a","source":{"kind":"arxiv","id":"1603.08661","version":2},"attestation_state":"computed","paper":{"title":"Regret Analysis of the Anytime Optimally Confident UCB Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Tor Lattimore","submitted_at":"2016-03-29T07:12:14Z","abstract_excerpt":"I introduce and analyse an anytime version of the Optimally Confident UCB (OCUCB) algorithm designed for minimising the cumulative regret in finite-armed stochastic bandits with subgaussian noise. The new algorithm is simple, intuitive (in hindsight) and comes with the strongest finite-time regret guarantees for a horizon-free algorithm so far. I also show a finite-time lower bound that nearly matches the upper bound."},"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":"1603.08661","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-29T07:12:14Z","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"title_canon_sha256":"f7fde99571e23ce529cdc7ab10c83981f24ec13184b56f9ce119198509ed820a","abstract_canon_sha256":"7976f0f9582c503d21bc7f2bd043e0c44363564e9082b96d1b5a64d5de75436c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:30.536111Z","signature_b64":"bsSgnw6Lj62ZR4gfJWcMWApmkK9eOZAHn2XfZWNsMYU2F0cYhyT+TaU20Uwdi9mKg/XVpQLyHa6aiCbhmPTVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b753f776050beba71e1bef3affb03df1dc93ca18cbf9cff1b87bb5d94a0c33a","last_reissued_at":"2026-05-18T01:15:30.535382Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:30.535382Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Regret Analysis of the Anytime Optimally Confident UCB Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Tor Lattimore","submitted_at":"2016-03-29T07:12:14Z","abstract_excerpt":"I introduce and analyse an anytime version of the Optimally Confident UCB (OCUCB) algorithm designed for minimising the cumulative regret in finite-armed stochastic bandits with subgaussian noise. The new algorithm is simple, intuitive (in hindsight) and comes with the strongest finite-time regret guarantees for a horizon-free algorithm so far. I also show a finite-time lower bound that nearly matches the upper bound."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.08661","kind":"arxiv","version":2},"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":"1603.08661","created_at":"2026-05-18T01:15:30.535507+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.08661v2","created_at":"2026-05-18T01:15:30.535507+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.08661","created_at":"2026-05-18T01:15:30.535507+00:00"},{"alias_kind":"pith_short_12","alias_value":"RN2T653AKC7L","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_16","alias_value":"RN2T653AKC7LU4PB","created_at":"2026-05-18T12:30:41.710351+00:00"},{"alias_kind":"pith_short_8","alias_value":"RN2T653A","created_at":"2026-05-18T12:30:41.710351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.06158","citing_title":"Accelerated learning from recommender systems using multi-armed bandit","ref_index":46,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RN2T653AKC7LU4PBX3Z276YD34","json":"https://pith.science/pith/RN2T653AKC7LU4PBX3Z276YD34.json","graph_json":"https://pith.science/api/pith-number/RN2T653AKC7LU4PBX3Z276YD34/graph.json","events_json":"https://pith.science/api/pith-number/RN2T653AKC7LU4PBX3Z276YD34/events.json","paper":"https://pith.science/paper/RN2T653A"},"agent_actions":{"view_html":"https://pith.science/pith/RN2T653AKC7LU4PBX3Z276YD34","download_json":"https://pith.science/pith/RN2T653AKC7LU4PBX3Z276YD34.json","view_paper":"https://pith.science/paper/RN2T653A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.08661&json=true","fetch_graph":"https://pith.science/api/pith-number/RN2T653AKC7LU4PBX3Z276YD34/graph.json","fetch_events":"https://pith.science/api/pith-number/RN2T653AKC7LU4PBX3Z276YD34/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RN2T653AKC7LU4PBX3Z276YD34/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RN2T653AKC7LU4PBX3Z276YD34/action/storage_attestation","attest_author":"https://pith.science/pith/RN2T653AKC7LU4PBX3Z276YD34/action/author_attestation","sign_citation":"https://pith.science/pith/RN2T653AKC7LU4PBX3Z276YD34/action/citation_signature","submit_replication":"https://pith.science/pith/RN2T653AKC7LU4PBX3Z276YD34/action/replication_record"}},"created_at":"2026-05-18T01:15:30.535507+00:00","updated_at":"2026-05-18T01:15:30.535507+00:00"}