{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NB4PPOBMNRJX2FBQVPLWSRLOQT","short_pith_number":"pith:NB4PPOBM","schema_version":"1.0","canonical_sha256":"6878f7b82c6c537d1430abd769456e84ffc3989330db897c17f36832bbd42332","source":{"kind":"arxiv","id":"2206.00466","version":2},"attestation_state":"computed","paper":{"title":"An $\\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Albert Thomas, Geovani Rizk, Igor Colin, Rida Laraki, Yann Chevaleyre","submitted_at":"2022-06-01T12:55:17Z","abstract_excerpt":"We propose the first regret-based approach to the Graphical Bilinear Bandits problem, where $n$ agents in a graph play a stochastic bilinear bandit game with each of their neighbors. This setting reveals a combinatorial NP-hard problem that prevents the use of any existing regret-based algorithm in the (bi-)linear bandit literature. In this paper, we fill this gap and present the first regret-based algorithm for graphical bilinear bandits using the principle of optimism in the face of uncertainty. Theoretical analysis of this new method yields an upper bound of $\\tilde{O}(\\sqrt{T})$ on the $\\a"},"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":"2206.00466","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-01T12:55:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"81ceb6774697de40b3b92a7ee8e0c0716bb7097a813d9aca0fd8c0cf5f7f5a13","abstract_canon_sha256":"00b0c91b4e5fe09b6bdd8855e21a1f929fe49b542627fb920c6c50dde7637314"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:05:47.983444Z","signature_b64":"VRrvD4KIwUKJId6VRo9Pb3C2GkLpqdy7fT+qkeo/Bacu1GuMQzPCbpMAH8UYrbwoiGSVR05U/IBKSXqWda5CAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6878f7b82c6c537d1430abd769456e84ffc3989330db897c17f36832bbd42332","last_reissued_at":"2026-07-05T05:05:47.982928Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:05:47.982928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An $\\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Albert Thomas, Geovani Rizk, Igor Colin, Rida Laraki, Yann Chevaleyre","submitted_at":"2022-06-01T12:55:17Z","abstract_excerpt":"We propose the first regret-based approach to the Graphical Bilinear Bandits problem, where $n$ agents in a graph play a stochastic bilinear bandit game with each of their neighbors. This setting reveals a combinatorial NP-hard problem that prevents the use of any existing regret-based algorithm in the (bi-)linear bandit literature. In this paper, we fill this gap and present the first regret-based algorithm for graphical bilinear bandits using the principle of optimism in the face of uncertainty. Theoretical analysis of this new method yields an upper bound of $\\tilde{O}(\\sqrt{T})$ on the $\\a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.00466","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2206.00466/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2206.00466","created_at":"2026-07-05T05:05:47.982989+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.00466v2","created_at":"2026-07-05T05:05:47.982989+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.00466","created_at":"2026-07-05T05:05:47.982989+00:00"},{"alias_kind":"pith_short_12","alias_value":"NB4PPOBMNRJX","created_at":"2026-07-05T05:05:47.982989+00:00"},{"alias_kind":"pith_short_16","alias_value":"NB4PPOBMNRJX2FBQ","created_at":"2026-07-05T05:05:47.982989+00:00"},{"alias_kind":"pith_short_8","alias_value":"NB4PPOBM","created_at":"2026-07-05T05:05:47.982989+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/NB4PPOBMNRJX2FBQVPLWSRLOQT","json":"https://pith.science/pith/NB4PPOBMNRJX2FBQVPLWSRLOQT.json","graph_json":"https://pith.science/api/pith-number/NB4PPOBMNRJX2FBQVPLWSRLOQT/graph.json","events_json":"https://pith.science/api/pith-number/NB4PPOBMNRJX2FBQVPLWSRLOQT/events.json","paper":"https://pith.science/paper/NB4PPOBM"},"agent_actions":{"view_html":"https://pith.science/pith/NB4PPOBMNRJX2FBQVPLWSRLOQT","download_json":"https://pith.science/pith/NB4PPOBMNRJX2FBQVPLWSRLOQT.json","view_paper":"https://pith.science/paper/NB4PPOBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.00466&json=true","fetch_graph":"https://pith.science/api/pith-number/NB4PPOBMNRJX2FBQVPLWSRLOQT/graph.json","fetch_events":"https://pith.science/api/pith-number/NB4PPOBMNRJX2FBQVPLWSRLOQT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NB4PPOBMNRJX2FBQVPLWSRLOQT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NB4PPOBMNRJX2FBQVPLWSRLOQT/action/storage_attestation","attest_author":"https://pith.science/pith/NB4PPOBMNRJX2FBQVPLWSRLOQT/action/author_attestation","sign_citation":"https://pith.science/pith/NB4PPOBMNRJX2FBQVPLWSRLOQT/action/citation_signature","submit_replication":"https://pith.science/pith/NB4PPOBMNRJX2FBQVPLWSRLOQT/action/replication_record"}},"created_at":"2026-07-05T05:05:47.982989+00:00","updated_at":"2026-07-05T05:05:47.982989+00:00"}