{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:VXZGZYSUBNXM3FYUAVMZQCKNI2","short_pith_number":"pith:VXZGZYSU","schema_version":"1.0","canonical_sha256":"adf26ce2540b6ecd9714055998094d468d03cc4697c4dfa6270b56b1d8135493","source":{"kind":"arxiv","id":"1906.00547","version":1},"attestation_state":"computed","paper":{"title":"MaxGap Bandit: Adaptive Algorithms for Approximate Ranking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ardhendu Tripathy, Robert Nowak, Sumeet Katariya","submitted_at":"2019-06-03T03:21:13Z","abstract_excerpt":"This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandit problem. This problem arises naturally in approximate ranking, noisy sorting, outlier detection, and top-arm identification in bandits. The key novelty of the MaxGap-bandit problem is that it aims to adaptively determine the natural partitioning of the distributions into a subset with larger means and a subset with smaller means, where the split is determined by the largest gap rather than a pre-specified rank or t"},"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":"1906.00547","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-06-03T03:21:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9bda4bc22ec06165496126d28e645dc7930b56b440a6135a774922019a4612a5","abstract_canon_sha256":"33959d855d1e114b8922720d8667b2e829347c1c9c051f32e4c54b40423be608"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:24.604771Z","signature_b64":"GWjszKJaezIQZCy5oqW0ZLO+uvIoYx8YCJn6Ovdfox22Lp7ebk7nVolGfMW2rQ5giQIgbiAw+5+z9r0NTmc7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"adf26ce2540b6ecd9714055998094d468d03cc4697c4dfa6270b56b1d8135493","last_reissued_at":"2026-05-17T23:44:24.604300Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:24.604300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MaxGap Bandit: Adaptive Algorithms for Approximate Ranking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ardhendu Tripathy, Robert Nowak, Sumeet Katariya","submitted_at":"2019-06-03T03:21:13Z","abstract_excerpt":"This paper studies the problem of adaptively sampling from K distributions (arms) in order to identify the largest gap between any two adjacent means. We call this the MaxGap-bandit problem. This problem arises naturally in approximate ranking, noisy sorting, outlier detection, and top-arm identification in bandits. The key novelty of the MaxGap-bandit problem is that it aims to adaptively determine the natural partitioning of the distributions into a subset with larger means and a subset with smaller means, where the split is determined by the largest gap rather than a pre-specified rank or t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00547","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":"1906.00547","created_at":"2026-05-17T23:44:24.604386+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.00547v1","created_at":"2026-05-17T23:44:24.604386+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00547","created_at":"2026-05-17T23:44:24.604386+00:00"},{"alias_kind":"pith_short_12","alias_value":"VXZGZYSUBNXM","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"VXZGZYSUBNXM3FYU","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"VXZGZYSU","created_at":"2026-05-18T12:33:30.264802+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/VXZGZYSUBNXM3FYUAVMZQCKNI2","json":"https://pith.science/pith/VXZGZYSUBNXM3FYUAVMZQCKNI2.json","graph_json":"https://pith.science/api/pith-number/VXZGZYSUBNXM3FYUAVMZQCKNI2/graph.json","events_json":"https://pith.science/api/pith-number/VXZGZYSUBNXM3FYUAVMZQCKNI2/events.json","paper":"https://pith.science/paper/VXZGZYSU"},"agent_actions":{"view_html":"https://pith.science/pith/VXZGZYSUBNXM3FYUAVMZQCKNI2","download_json":"https://pith.science/pith/VXZGZYSUBNXM3FYUAVMZQCKNI2.json","view_paper":"https://pith.science/paper/VXZGZYSU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.00547&json=true","fetch_graph":"https://pith.science/api/pith-number/VXZGZYSUBNXM3FYUAVMZQCKNI2/graph.json","fetch_events":"https://pith.science/api/pith-number/VXZGZYSUBNXM3FYUAVMZQCKNI2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VXZGZYSUBNXM3FYUAVMZQCKNI2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VXZGZYSUBNXM3FYUAVMZQCKNI2/action/storage_attestation","attest_author":"https://pith.science/pith/VXZGZYSUBNXM3FYUAVMZQCKNI2/action/author_attestation","sign_citation":"https://pith.science/pith/VXZGZYSUBNXM3FYUAVMZQCKNI2/action/citation_signature","submit_replication":"https://pith.science/pith/VXZGZYSUBNXM3FYUAVMZQCKNI2/action/replication_record"}},"created_at":"2026-05-17T23:44:24.604386+00:00","updated_at":"2026-05-17T23:44:24.604386+00:00"}