{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OQDDH55FK6KBGC6ZI5LI7TGI2Z","short_pith_number":"pith:OQDDH55F","schema_version":"1.0","canonical_sha256":"740633f7a55794130bd947568fccc8d66bd4f7aad19fc66134bb3348b5842107","source":{"kind":"arxiv","id":"1711.04454","version":2},"attestation_state":"computed","paper":{"title":"Thresholding Bandit for Dose-ranging: The Impact of Monotonicity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Aur\\'elien Garivier (IMT), Laurent Rossi (IMT), Pierre M\\'enard (IMT), Pierre Menard (IMT)","submitted_at":"2017-11-13T07:36:01Z","abstract_excerpt":"We analyze the sample complexity of the thresholding bandit problem, with and without the assumption that the mean values of the arms are increasing. In each case, we provide a lower bound valid for any risk $\\delta$ and any $\\delta$-correct algorithm; in addition, we propose an algorithm whose sample complexity is of the same order of magnitude for small risks. This work is motivated by phase 1 clinical trials, a practically important setting where the arm means are increasing by nature, and where no satisfactory solution is available so far."},"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":"1711.04454","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-11-13T07:36:01Z","cross_cats_sorted":["stat.ML","stat.TH"],"title_canon_sha256":"89b32c68dcf9514ef86de23b967ffabd505efbc9d30fed9cfa0d2f67d6354896","abstract_canon_sha256":"0f77b524786aad9830f211af6a11596a51c755724219e84160dc8d99a9764ed9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:03.706982Z","signature_b64":"2lk/TvEGEOBoNcDB5R5/Q9ajcwvfe/Xt5orMV9b+oshuGow5I932dbt/0QbgU7e5fcxowjmv7WDOjvXTPNdnCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"740633f7a55794130bd947568fccc8d66bd4f7aad19fc66134bb3348b5842107","last_reissued_at":"2026-05-18T00:10:03.706249Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:03.706249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Thresholding Bandit for Dose-ranging: The Impact of Monotonicity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Aur\\'elien Garivier (IMT), Laurent Rossi (IMT), Pierre M\\'enard (IMT), Pierre Menard (IMT)","submitted_at":"2017-11-13T07:36:01Z","abstract_excerpt":"We analyze the sample complexity of the thresholding bandit problem, with and without the assumption that the mean values of the arms are increasing. In each case, we provide a lower bound valid for any risk $\\delta$ and any $\\delta$-correct algorithm; in addition, we propose an algorithm whose sample complexity is of the same order of magnitude for small risks. This work is motivated by phase 1 clinical trials, a practically important setting where the arm means are increasing by nature, and where no satisfactory solution is available so far."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.04454","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":"1711.04454","created_at":"2026-05-18T00:10:03.706371+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.04454v2","created_at":"2026-05-18T00:10:03.706371+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.04454","created_at":"2026-05-18T00:10:03.706371+00:00"},{"alias_kind":"pith_short_12","alias_value":"OQDDH55FK6KB","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OQDDH55FK6KBGC6Z","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OQDDH55F","created_at":"2026-05-18T12:31:34.259226+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.10431","citing_title":"Non-Asymptotic Pure Exploration by Solving Games","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OQDDH55FK6KBGC6ZI5LI7TGI2Z","json":"https://pith.science/pith/OQDDH55FK6KBGC6ZI5LI7TGI2Z.json","graph_json":"https://pith.science/api/pith-number/OQDDH55FK6KBGC6ZI5LI7TGI2Z/graph.json","events_json":"https://pith.science/api/pith-number/OQDDH55FK6KBGC6ZI5LI7TGI2Z/events.json","paper":"https://pith.science/paper/OQDDH55F"},"agent_actions":{"view_html":"https://pith.science/pith/OQDDH55FK6KBGC6ZI5LI7TGI2Z","download_json":"https://pith.science/pith/OQDDH55FK6KBGC6ZI5LI7TGI2Z.json","view_paper":"https://pith.science/paper/OQDDH55F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.04454&json=true","fetch_graph":"https://pith.science/api/pith-number/OQDDH55FK6KBGC6ZI5LI7TGI2Z/graph.json","fetch_events":"https://pith.science/api/pith-number/OQDDH55FK6KBGC6ZI5LI7TGI2Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OQDDH55FK6KBGC6ZI5LI7TGI2Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OQDDH55FK6KBGC6ZI5LI7TGI2Z/action/storage_attestation","attest_author":"https://pith.science/pith/OQDDH55FK6KBGC6ZI5LI7TGI2Z/action/author_attestation","sign_citation":"https://pith.science/pith/OQDDH55FK6KBGC6ZI5LI7TGI2Z/action/citation_signature","submit_replication":"https://pith.science/pith/OQDDH55FK6KBGC6ZI5LI7TGI2Z/action/replication_record"}},"created_at":"2026-05-18T00:10:03.706371+00:00","updated_at":"2026-05-18T00:10:03.706371+00:00"}