{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:KOVSAX4RIL53B4GCX5B2INO6RY","short_pith_number":"pith:KOVSAX4R","schema_version":"1.0","canonical_sha256":"53ab205f9142fbb0f0c2bf43a435de8e35eea4badc2fba8cdda5c16a47cf73cf","source":{"kind":"arxiv","id":"1511.08681","version":1},"attestation_state":"computed","paper":{"title":"Algorithms for Differentially Private Multi-Armed Bandits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"stat.ML","authors_text":"Aristide Tossou, Christos Dimitrakakis","submitted_at":"2015-11-27T14:16:00Z","abstract_excerpt":"We present differentially private algorithms for the stochastic Multi-Armed Bandit (MAB) problem. This is a problem for applications such as adaptive clinical trials, experiment design, and user-targeted advertising where private information is connected to individual rewards. Our major contribution is to show that there exist $(\\epsilon, \\delta)$ differentially private variants of Upper Confidence Bound algorithms which have optimal regret, $O(\\epsilon^{-1} + \\log T)$. This is a significant improvement over previous results, which only achieve poly-log regret $O(\\epsilon^{-2} \\log^{2} T)$, be"},"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":"1511.08681","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-11-27T14:16:00Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"22953464b83b2d6c928b7a0cb2819957a60f10768f249cd06b672ee4fdbc285c","abstract_canon_sha256":"d14691f6c7e586656e2dac6c81ef96d74aa588a7eb5142aea9e0113950b00a2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:25:48.324262Z","signature_b64":"z7YH6pHSjn4okq9quhCIOQDjr26gEK/XSnBJe0jAhZW/tCXJsc8Wt8UxHCR1FgyM7GXiqyp6Je4iKBdRqNFbCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53ab205f9142fbb0f0c2bf43a435de8e35eea4badc2fba8cdda5c16a47cf73cf","last_reissued_at":"2026-05-18T01:25:48.323715Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:25:48.323715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Algorithms for Differentially Private Multi-Armed Bandits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"stat.ML","authors_text":"Aristide Tossou, Christos Dimitrakakis","submitted_at":"2015-11-27T14:16:00Z","abstract_excerpt":"We present differentially private algorithms for the stochastic Multi-Armed Bandit (MAB) problem. This is a problem for applications such as adaptive clinical trials, experiment design, and user-targeted advertising where private information is connected to individual rewards. Our major contribution is to show that there exist $(\\epsilon, \\delta)$ differentially private variants of Upper Confidence Bound algorithms which have optimal regret, $O(\\epsilon^{-1} + \\log T)$. This is a significant improvement over previous results, which only achieve poly-log regret $O(\\epsilon^{-2} \\log^{2} T)$, be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.08681","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":"1511.08681","created_at":"2026-05-18T01:25:48.323782+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.08681v1","created_at":"2026-05-18T01:25:48.323782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.08681","created_at":"2026-05-18T01:25:48.323782+00:00"},{"alias_kind":"pith_short_12","alias_value":"KOVSAX4RIL53","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_16","alias_value":"KOVSAX4RIL53B4GC","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_8","alias_value":"KOVSAX4R","created_at":"2026-05-18T12:29:29.992203+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.23887","citing_title":"CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KOVSAX4RIL53B4GCX5B2INO6RY","json":"https://pith.science/pith/KOVSAX4RIL53B4GCX5B2INO6RY.json","graph_json":"https://pith.science/api/pith-number/KOVSAX4RIL53B4GCX5B2INO6RY/graph.json","events_json":"https://pith.science/api/pith-number/KOVSAX4RIL53B4GCX5B2INO6RY/events.json","paper":"https://pith.science/paper/KOVSAX4R"},"agent_actions":{"view_html":"https://pith.science/pith/KOVSAX4RIL53B4GCX5B2INO6RY","download_json":"https://pith.science/pith/KOVSAX4RIL53B4GCX5B2INO6RY.json","view_paper":"https://pith.science/paper/KOVSAX4R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.08681&json=true","fetch_graph":"https://pith.science/api/pith-number/KOVSAX4RIL53B4GCX5B2INO6RY/graph.json","fetch_events":"https://pith.science/api/pith-number/KOVSAX4RIL53B4GCX5B2INO6RY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KOVSAX4RIL53B4GCX5B2INO6RY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KOVSAX4RIL53B4GCX5B2INO6RY/action/storage_attestation","attest_author":"https://pith.science/pith/KOVSAX4RIL53B4GCX5B2INO6RY/action/author_attestation","sign_citation":"https://pith.science/pith/KOVSAX4RIL53B4GCX5B2INO6RY/action/citation_signature","submit_replication":"https://pith.science/pith/KOVSAX4RIL53B4GCX5B2INO6RY/action/replication_record"}},"created_at":"2026-05-18T01:25:48.323782+00:00","updated_at":"2026-05-18T01:25:48.323782+00:00"}