{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:EUIJKJIWGI4FQB5VFB5YWQJAAY","short_pith_number":"pith:EUIJKJIW","canonical_record":{"source":{"id":"1507.04523","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-16T11:02:13Z","cross_cats_sorted":[],"title_canon_sha256":"e2505a543c87462e941e1ca7a6bb5e9f8608d9990543863143bb44841910464d","abstract_canon_sha256":"96e7b5e3b088a7dea61466cc9d2d4723783d33970a67699aee5f580e58683f36"},"schema_version":"1.0"},"canonical_sha256":"251095251632385807b5287b8b412006237930a186ba09cdf68ebbd10a47e75d","source":{"kind":"arxiv","id":"1507.04523","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.04523","created_at":"2026-05-18T01:36:47Z"},{"alias_kind":"arxiv_version","alias_value":"1507.04523v1","created_at":"2026-05-18T01:36:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.04523","created_at":"2026-05-18T01:36:47Z"},{"alias_kind":"pith_short_12","alias_value":"EUIJKJIWGI4F","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"EUIJKJIWGI4FQB5V","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"EUIJKJIW","created_at":"2026-05-18T12:29:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:EUIJKJIWGI4FQB5VFB5YWQJAAY","target":"record","payload":{"canonical_record":{"source":{"id":"1507.04523","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-16T11:02:13Z","cross_cats_sorted":[],"title_canon_sha256":"e2505a543c87462e941e1ca7a6bb5e9f8608d9990543863143bb44841910464d","abstract_canon_sha256":"96e7b5e3b088a7dea61466cc9d2d4723783d33970a67699aee5f580e58683f36"},"schema_version":"1.0"},"canonical_sha256":"251095251632385807b5287b8b412006237930a186ba09cdf68ebbd10a47e75d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:47.091918Z","signature_b64":"NleqjsVrrl8211ZG0vpSzcz6gbSlubV6+QkuTSs3bEphaDtOdOUUEYfcBOai+6EFZSkAGUKTkpf7bEK6mBI6Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"251095251632385807b5287b8b412006237930a186ba09cdf68ebbd10a47e75d","last_reissued_at":"2026-05-18T01:36:47.091411Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:47.091411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1507.04523","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:36:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/kreXN4iMnOr14rICV70D6mLAPKADn/+L5MU38uYnG6adK/kfa48iVDJV5aMfSl9VKYgeuq3afRzFAg0Ywi2CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:33:32.557599Z"},"content_sha256":"70f52794f0f785fa7651a6da987f0e57333ba510feeed890b484e07b52d361e5","schema_version":"1.0","event_id":"sha256:70f52794f0f785fa7651a6da987f0e57333ba510feeed890b484e07b52d361e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:EUIJKJIWGI4FQB5VFB5YWQJAAY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alessandro Lazaric, Alexandra Carpentier, Andr\\'as Antos, Mohammad Ghavamzadeh, Peter Auer, R\\'emi Munos","submitted_at":"2015-07-16T11:02:13Z","abstract_excerpt":"In this paper, we study the problem of estimating uniformly well the mean values of several distributions given a finite budget of samples. If the variance of the distributions were known, one could design an optimal sampling strategy by collecting a number of independent samples per distribution that is proportional to their variance. However, in the more realistic case where the distributions are not known in advance, one needs to design adaptive sampling strategies in order to select which distribution to sample from according to the previously observed samples. We describe two strategies b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.04523","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:36:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hWZOQ5uBoh4auFyVaELGUbqKKuZ+GlyPIqxIIra5ExqakWMZXc6dvCOGj6S+haECXUYPeGjzsVdovP2/70ERCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T05:33:32.558268Z"},"content_sha256":"9371b9cf01b1a35a0fa7379e14dc026b995a1c57f40c758ad6b84e7ac25e0830","schema_version":"1.0","event_id":"sha256:9371b9cf01b1a35a0fa7379e14dc026b995a1c57f40c758ad6b84e7ac25e0830"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EUIJKJIWGI4FQB5VFB5YWQJAAY/bundle.json","state_url":"https://pith.science/pith/EUIJKJIWGI4FQB5VFB5YWQJAAY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EUIJKJIWGI4FQB5VFB5YWQJAAY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T05:33:32Z","links":{"resolver":"https://pith.science/pith/EUIJKJIWGI4FQB5VFB5YWQJAAY","bundle":"https://pith.science/pith/EUIJKJIWGI4FQB5VFB5YWQJAAY/bundle.json","state":"https://pith.science/pith/EUIJKJIWGI4FQB5VFB5YWQJAAY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EUIJKJIWGI4FQB5VFB5YWQJAAY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:EUIJKJIWGI4FQB5VFB5YWQJAAY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"96e7b5e3b088a7dea61466cc9d2d4723783d33970a67699aee5f580e58683f36","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-16T11:02:13Z","title_canon_sha256":"e2505a543c87462e941e1ca7a6bb5e9f8608d9990543863143bb44841910464d"},"schema_version":"1.0","source":{"id":"1507.04523","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.04523","created_at":"2026-05-18T01:36:47Z"},{"alias_kind":"arxiv_version","alias_value":"1507.04523v1","created_at":"2026-05-18T01:36:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.04523","created_at":"2026-05-18T01:36:47Z"},{"alias_kind":"pith_short_12","alias_value":"EUIJKJIWGI4F","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_16","alias_value":"EUIJKJIWGI4FQB5V","created_at":"2026-05-18T12:29:19Z"},{"alias_kind":"pith_short_8","alias_value":"EUIJKJIW","created_at":"2026-05-18T12:29:19Z"}],"graph_snapshots":[{"event_id":"sha256:9371b9cf01b1a35a0fa7379e14dc026b995a1c57f40c758ad6b84e7ac25e0830","target":"graph","created_at":"2026-05-18T01:36:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In this paper, we study the problem of estimating uniformly well the mean values of several distributions given a finite budget of samples. If the variance of the distributions were known, one could design an optimal sampling strategy by collecting a number of independent samples per distribution that is proportional to their variance. However, in the more realistic case where the distributions are not known in advance, one needs to design adaptive sampling strategies in order to select which distribution to sample from according to the previously observed samples. We describe two strategies b","authors_text":"Alessandro Lazaric, Alexandra Carpentier, Andr\\'as Antos, Mohammad Ghavamzadeh, Peter Auer, R\\'emi Munos","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-16T11:02:13Z","title":"Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.04523","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:70f52794f0f785fa7651a6da987f0e57333ba510feeed890b484e07b52d361e5","target":"record","created_at":"2026-05-18T01:36:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"96e7b5e3b088a7dea61466cc9d2d4723783d33970a67699aee5f580e58683f36","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-07-16T11:02:13Z","title_canon_sha256":"e2505a543c87462e941e1ca7a6bb5e9f8608d9990543863143bb44841910464d"},"schema_version":"1.0","source":{"id":"1507.04523","kind":"arxiv","version":1}},"canonical_sha256":"251095251632385807b5287b8b412006237930a186ba09cdf68ebbd10a47e75d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"251095251632385807b5287b8b412006237930a186ba09cdf68ebbd10a47e75d","first_computed_at":"2026-05-18T01:36:47.091411Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:36:47.091411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NleqjsVrrl8211ZG0vpSzcz6gbSlubV6+QkuTSs3bEphaDtOdOUUEYfcBOai+6EFZSkAGUKTkpf7bEK6mBI6Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:36:47.091918Z","signed_message":"canonical_sha256_bytes"},"source_id":"1507.04523","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70f52794f0f785fa7651a6da987f0e57333ba510feeed890b484e07b52d361e5","sha256:9371b9cf01b1a35a0fa7379e14dc026b995a1c57f40c758ad6b84e7ac25e0830"],"state_sha256":"d4826606dc4dc392274bf1dbdda9dda72a0e745c2b0dc569ceafb954cf2f7d1d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4/X7kGTkMof97w/nZZrqQt/8RhX6Pek5n+gD4SevTieutIHRp5tVeGRMtvu217FmQn9NilEnFFhBIlTDwXJ0Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T05:33:32.561370Z","bundle_sha256":"4c543a9edb18be37aac99979667902e9b21b09924bffd7427f0a2ebc8d82e23a"}}