{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GN5F3J676MXJLJ64AEZASEELNG","short_pith_number":"pith:GN5F3J67","schema_version":"1.0","canonical_sha256":"337a5da7dff32e95a7dc013209108b69ad0734d910147df6b48a638ca432004c","source":{"kind":"arxiv","id":"1808.04008","version":3},"attestation_state":"computed","paper":{"title":"PAC Battling Bandits in the Plackett-Luce Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aadirupa Saha, Aditya Gopalan","submitted_at":"2018-08-12T22:23:43Z","abstract_excerpt":"We introduce the probably approximately correct (PAC) \\emph{Battling-Bandit} problem with the Plackett-Luce (PL) subset choice model--an online learning framework where at each trial the learner chooses a subset of $k$ arms from a fixed set of $n$ arms, and subsequently observes a stochastic feedback indicating preference information of the items in the chosen subset, e.g., the most preferred item or ranking of the top $m$ most preferred items etc. The objective is to identify a near-best item in the underlying PL model with high confidence. This generalizes the well-studied PAC \\emph{Dueling-"},"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":"1808.04008","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-12T22:23:43Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8677645619abf97475fd8d3fffc88c1c4123335c590c710220b58e2bcfd234e1","abstract_canon_sha256":"06203692e91b39d1b88cb4e3c2c52d2cbdb1c06546044194d549ee20db5688cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:15.775458Z","signature_b64":"1pg3qLMhwmEg7fsrkobZdz8DHgGR3DkJQSx0ek4pCsqZSm9sQnEn8osYCNzHsKSzZDju92T2Vcl88KB5uJ1mAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"337a5da7dff32e95a7dc013209108b69ad0734d910147df6b48a638ca432004c","last_reissued_at":"2026-05-17T23:52:15.774814Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:15.774814Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PAC Battling Bandits in the Plackett-Luce Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aadirupa Saha, Aditya Gopalan","submitted_at":"2018-08-12T22:23:43Z","abstract_excerpt":"We introduce the probably approximately correct (PAC) \\emph{Battling-Bandit} problem with the Plackett-Luce (PL) subset choice model--an online learning framework where at each trial the learner chooses a subset of $k$ arms from a fixed set of $n$ arms, and subsequently observes a stochastic feedback indicating preference information of the items in the chosen subset, e.g., the most preferred item or ranking of the top $m$ most preferred items etc. The objective is to identify a near-best item in the underlying PL model with high confidence. This generalizes the well-studied PAC \\emph{Dueling-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04008","kind":"arxiv","version":3},"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":"1808.04008","created_at":"2026-05-17T23:52:15.774910+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.04008v3","created_at":"2026-05-17T23:52:15.774910+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04008","created_at":"2026-05-17T23:52:15.774910+00:00"},{"alias_kind":"pith_short_12","alias_value":"GN5F3J676MXJ","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GN5F3J676MXJLJ64","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GN5F3J67","created_at":"2026-05-18T12:32:25.280505+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/GN5F3J676MXJLJ64AEZASEELNG","json":"https://pith.science/pith/GN5F3J676MXJLJ64AEZASEELNG.json","graph_json":"https://pith.science/api/pith-number/GN5F3J676MXJLJ64AEZASEELNG/graph.json","events_json":"https://pith.science/api/pith-number/GN5F3J676MXJLJ64AEZASEELNG/events.json","paper":"https://pith.science/paper/GN5F3J67"},"agent_actions":{"view_html":"https://pith.science/pith/GN5F3J676MXJLJ64AEZASEELNG","download_json":"https://pith.science/pith/GN5F3J676MXJLJ64AEZASEELNG.json","view_paper":"https://pith.science/paper/GN5F3J67","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.04008&json=true","fetch_graph":"https://pith.science/api/pith-number/GN5F3J676MXJLJ64AEZASEELNG/graph.json","fetch_events":"https://pith.science/api/pith-number/GN5F3J676MXJLJ64AEZASEELNG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GN5F3J676MXJLJ64AEZASEELNG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GN5F3J676MXJLJ64AEZASEELNG/action/storage_attestation","attest_author":"https://pith.science/pith/GN5F3J676MXJLJ64AEZASEELNG/action/author_attestation","sign_citation":"https://pith.science/pith/GN5F3J676MXJLJ64AEZASEELNG/action/citation_signature","submit_replication":"https://pith.science/pith/GN5F3J676MXJLJ64AEZASEELNG/action/replication_record"}},"created_at":"2026-05-17T23:52:15.774910+00:00","updated_at":"2026-05-17T23:52:15.774910+00:00"}