{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:R3AEVFPPP3E3Z2KICK5B4FT3H7","short_pith_number":"pith:R3AEVFPP","canonical_record":{"source":{"id":"1305.1809","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-05-08T13:11:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"55dae8a418cafdc816ae4ad416537a0bdbf1d5d38ee551e5612d3dae31d124bc","abstract_canon_sha256":"c908b3cdd472cdc0470ae70e02643db25fae9c6d4487d2e9e5f2361e43b991fb"},"schema_version":"1.0"},"canonical_sha256":"8ec04a95ef7ec9bce94812ba1e167b3ff7ad0c59b3d80d2138aed5e702bbd94f","source":{"kind":"arxiv","id":"1305.1809","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1305.1809","created_at":"2026-05-18T02:52:48Z"},{"alias_kind":"arxiv_version","alias_value":"1305.1809v2","created_at":"2026-05-18T02:52:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1305.1809","created_at":"2026-05-18T02:52:48Z"},{"alias_kind":"pith_short_12","alias_value":"R3AEVFPPP3E3","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_16","alias_value":"R3AEVFPPP3E3Z2KI","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_8","alias_value":"R3AEVFPP","created_at":"2026-05-18T12:27:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:R3AEVFPPP3E3Z2KICK5B4FT3H7","target":"record","payload":{"canonical_record":{"source":{"id":"1305.1809","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-05-08T13:11:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"55dae8a418cafdc816ae4ad416537a0bdbf1d5d38ee551e5612d3dae31d124bc","abstract_canon_sha256":"c908b3cdd472cdc0470ae70e02643db25fae9c6d4487d2e9e5f2361e43b991fb"},"schema_version":"1.0"},"canonical_sha256":"8ec04a95ef7ec9bce94812ba1e167b3ff7ad0c59b3d80d2138aed5e702bbd94f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:52:48.534483Z","signature_b64":"Sz45DZEJoF2uxiTGp1rl22X3yUs9y5oe5Gtk/zQcjWRtmFmTCJMLK1H6Enu6IHcB1SqDDGXidJZ8ZQ1zgVGnBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ec04a95ef7ec9bce94812ba1e167b3ff7ad0c59b3d80d2138aed5e702bbd94f","last_reissued_at":"2026-05-18T02:52:48.533883Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:52:48.533883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1305.1809","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:52:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cfFCL927onO8vpcCecCnuWfg+fQ+IzTIJE6X1j4nxM7zJwFE8+orIi/8lRVwEC0PdJYf6cKHbwA6f6OTvSoWAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T14:44:55.832591Z"},"content_sha256":"856d86cca8f4d5c970707a2cf4441cce28878319b129240b1a009e648f9c6a70","schema_version":"1.0","event_id":"sha256:856d86cca8f4d5c970707a2cf4441cce28878319b129240b1a009e648f9c6a70"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:R3AEVFPPP3E3Z2KICK5B4FT3H7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Cover Tree Bayesian Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Christos Dimitrakakis, Konstantinos Blekas, Nikolaos Tziortziotis","submitted_at":"2013-05-08T13:11:52Z","abstract_excerpt":"This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed form. The tree structure itself is constructed using the cover tree method, which remains efficient in high dimensional spaces. We combine the model with Thompson sampling and approximate dynamic programming to obtain effective exploration policies in unknown environments. The flexibility and computational simplicity of the model render it sui"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1305.1809","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:52:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wh/fRVg52AJigxZ0ebP8LT7OOtpX2iJvrWZs2qBStDa6TcM3+2e3k3vNvLKydLhfbXQ+rQENJimN9x1rPQmZBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T14:44:55.832930Z"},"content_sha256":"d3bb056a89bd6be99c9dac2bfe7fdafcd94a9d9e409f0684cd0c41159a52c5db","schema_version":"1.0","event_id":"sha256:d3bb056a89bd6be99c9dac2bfe7fdafcd94a9d9e409f0684cd0c41159a52c5db"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R3AEVFPPP3E3Z2KICK5B4FT3H7/bundle.json","state_url":"https://pith.science/pith/R3AEVFPPP3E3Z2KICK5B4FT3H7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R3AEVFPPP3E3Z2KICK5B4FT3H7/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":"2026-06-06T14:44:55Z","links":{"resolver":"https://pith.science/pith/R3AEVFPPP3E3Z2KICK5B4FT3H7","bundle":"https://pith.science/pith/R3AEVFPPP3E3Z2KICK5B4FT3H7/bundle.json","state":"https://pith.science/pith/R3AEVFPPP3E3Z2KICK5B4FT3H7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R3AEVFPPP3E3Z2KICK5B4FT3H7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:R3AEVFPPP3E3Z2KICK5B4FT3H7","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":"c908b3cdd472cdc0470ae70e02643db25fae9c6d4487d2e9e5f2361e43b991fb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-05-08T13:11:52Z","title_canon_sha256":"55dae8a418cafdc816ae4ad416537a0bdbf1d5d38ee551e5612d3dae31d124bc"},"schema_version":"1.0","source":{"id":"1305.1809","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1305.1809","created_at":"2026-05-18T02:52:48Z"},{"alias_kind":"arxiv_version","alias_value":"1305.1809v2","created_at":"2026-05-18T02:52:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1305.1809","created_at":"2026-05-18T02:52:48Z"},{"alias_kind":"pith_short_12","alias_value":"R3AEVFPPP3E3","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_16","alias_value":"R3AEVFPPP3E3Z2KI","created_at":"2026-05-18T12:27:57Z"},{"alias_kind":"pith_short_8","alias_value":"R3AEVFPP","created_at":"2026-05-18T12:27:57Z"}],"graph_snapshots":[{"event_id":"sha256:d3bb056a89bd6be99c9dac2bfe7fdafcd94a9d9e409f0684cd0c41159a52c5db","target":"graph","created_at":"2026-05-18T02:52:48Z","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":"This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian piecewise-linear models, which can be updated in closed form. The tree structure itself is constructed using the cover tree method, which remains efficient in high dimensional spaces. We combine the model with Thompson sampling and approximate dynamic programming to obtain effective exploration policies in unknown environments. The flexibility and computational simplicity of the model render it sui","authors_text":"Christos Dimitrakakis, Konstantinos Blekas, Nikolaos Tziortziotis","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-05-08T13:11:52Z","title":"Cover Tree Bayesian Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1305.1809","kind":"arxiv","version":2},"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":"sha256:856d86cca8f4d5c970707a2cf4441cce28878319b129240b1a009e648f9c6a70","target":"record","created_at":"2026-05-18T02:52:48Z","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":"c908b3cdd472cdc0470ae70e02643db25fae9c6d4487d2e9e5f2361e43b991fb","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-05-08T13:11:52Z","title_canon_sha256":"55dae8a418cafdc816ae4ad416537a0bdbf1d5d38ee551e5612d3dae31d124bc"},"schema_version":"1.0","source":{"id":"1305.1809","kind":"arxiv","version":2}},"canonical_sha256":"8ec04a95ef7ec9bce94812ba1e167b3ff7ad0c59b3d80d2138aed5e702bbd94f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ec04a95ef7ec9bce94812ba1e167b3ff7ad0c59b3d80d2138aed5e702bbd94f","first_computed_at":"2026-05-18T02:52:48.533883Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:52:48.533883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Sz45DZEJoF2uxiTGp1rl22X3yUs9y5oe5Gtk/zQcjWRtmFmTCJMLK1H6Enu6IHcB1SqDDGXidJZ8ZQ1zgVGnBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:52:48.534483Z","signed_message":"canonical_sha256_bytes"},"source_id":"1305.1809","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:856d86cca8f4d5c970707a2cf4441cce28878319b129240b1a009e648f9c6a70","sha256:d3bb056a89bd6be99c9dac2bfe7fdafcd94a9d9e409f0684cd0c41159a52c5db"],"state_sha256":"327d8a5ee0ba7d1e81ad532446ad0c001999aad4629f99bb34d133f30a29d2d5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/Nw04/zunqE8bdjUkdCkJzrJ/t1PU/Oa49rfqtBpTJw34esEyuvTfFx+Icm4UlZWhIkv2RWSK95L/p5LUdknBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T14:44:55.834836Z","bundle_sha256":"cfe1d5b8f714cc84719b27d4c16b341d47c6cd2afba734ab04fa795d53dbc3db"}}