{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:GVYEH25PCTKU2TWOME6Y5COQ5U","short_pith_number":"pith:GVYEH25P","canonical_record":{"source":{"id":"2110.10133","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-19T17:44:09Z","cross_cats_sorted":["cs.CR","math.OC","stat.ML"],"title_canon_sha256":"22e2e178f0a46a7fe1ad7ef793948d9c816c9d9978f38ff9195d69cdef5b0295","abstract_canon_sha256":"ea5f66241d274e00cc9564789ad419b10720c5c87067897b6a13fbe9ffc6c94e"},"schema_version":"1.0"},"canonical_sha256":"357043ebaf14d54d4ece613d8e89d0ed160d26ce276fbc87d4ff138a35b2abd9","source":{"kind":"arxiv","id":"2110.10133","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.10133","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"arxiv_version","alias_value":"2110.10133v1","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.10133","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"pith_short_12","alias_value":"GVYEH25PCTKU","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"pith_short_16","alias_value":"GVYEH25PCTKU2TWO","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"pith_short_8","alias_value":"GVYEH25P","created_at":"2026-07-05T03:24:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:GVYEH25PCTKU2TWOME6Y5COQ5U","target":"record","payload":{"canonical_record":{"source":{"id":"2110.10133","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-19T17:44:09Z","cross_cats_sorted":["cs.CR","math.OC","stat.ML"],"title_canon_sha256":"22e2e178f0a46a7fe1ad7ef793948d9c816c9d9978f38ff9195d69cdef5b0295","abstract_canon_sha256":"ea5f66241d274e00cc9564789ad419b10720c5c87067897b6a13fbe9ffc6c94e"},"schema_version":"1.0"},"canonical_sha256":"357043ebaf14d54d4ece613d8e89d0ed160d26ce276fbc87d4ff138a35b2abd9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:24:04.291324Z","signature_b64":"xRkVTb404warRgWSTDa+EYXJEz+wY/WdEDsBb9Mx8bJ360rNi/Kaqe/M4ucnrgWdDANfL6sd5/G4zklaw6emBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"357043ebaf14d54d4ece613d8e89d0ed160d26ce276fbc87d4ff138a35b2abd9","last_reissued_at":"2026-07-05T03:24:04.290880Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:24:04.290880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.10133","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":"2026-07-05T03:24:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cX/fujT9n5RKPDHlinErEgedW8r14vRcPwXHHyIjvVD9dkgOrpAILaXqun+mfv+N1dnzwGJUjB5kghpBa5QVDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:49:26.113292Z"},"content_sha256":"ce6983bd230989f02df9c3bd3406460a3ce0f58249f8a4937461f8a4b56d60f0","schema_version":"1.0","event_id":"sha256:ce6983bd230989f02df9c3bd3406460a3ce0f58249f8a4937461f8a4b56d60f0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:GVYEH25PCTKU2TWOME6Y5COQ5U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chonghua Liao, Jiafan He, Quanquan Gu","submitted_at":"2021-10-19T17:44:09Z","abstract_excerpt":"Reinforcement learning (RL) algorithms can be used to provide personalized services, which rely on users' private and sensitive data. To protect the users' privacy, privacy-preserving RL algorithms are in demand. In this paper, we study RL with linear function approximation and local differential privacy (LDP) guarantees. We propose a novel $(\\varepsilon, \\delta)$-LDP algorithm for learning a class of Markov decision processes (MDPs) dubbed linear mixture MDPs, and obtains an $\\tilde{\\mathcal{O}}( d^{5/4}H^{7/4}T^{3/4}\\left(\\log(1/\\delta)\\right)^{1/4}\\sqrt{1/\\varepsilon})$ regret, where $d$ is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.10133","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2110.10133/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T03:24:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P1h2TpRn3/7gN8nJXYoBXyyQNkQAJ0pqmnrTE4Buit4i4BvgguaXEorgqZ7DQzaggBRg8s6vziMF1gSd2ywQAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:49:26.113695Z"},"content_sha256":"bae216b9c189c33c7ff2ee63c9fa86bb1dcea5a376b389c990488d18081321c1","schema_version":"1.0","event_id":"sha256:bae216b9c189c33c7ff2ee63c9fa86bb1dcea5a376b389c990488d18081321c1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GVYEH25PCTKU2TWOME6Y5COQ5U/bundle.json","state_url":"https://pith.science/pith/GVYEH25PCTKU2TWOME6Y5COQ5U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GVYEH25PCTKU2TWOME6Y5COQ5U/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":"2026-07-06T08:49:26Z","links":{"resolver":"https://pith.science/pith/GVYEH25PCTKU2TWOME6Y5COQ5U","bundle":"https://pith.science/pith/GVYEH25PCTKU2TWOME6Y5COQ5U/bundle.json","state":"https://pith.science/pith/GVYEH25PCTKU2TWOME6Y5COQ5U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GVYEH25PCTKU2TWOME6Y5COQ5U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:GVYEH25PCTKU2TWOME6Y5COQ5U","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":"ea5f66241d274e00cc9564789ad419b10720c5c87067897b6a13fbe9ffc6c94e","cross_cats_sorted":["cs.CR","math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-19T17:44:09Z","title_canon_sha256":"22e2e178f0a46a7fe1ad7ef793948d9c816c9d9978f38ff9195d69cdef5b0295"},"schema_version":"1.0","source":{"id":"2110.10133","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.10133","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"arxiv_version","alias_value":"2110.10133v1","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.10133","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"pith_short_12","alias_value":"GVYEH25PCTKU","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"pith_short_16","alias_value":"GVYEH25PCTKU2TWO","created_at":"2026-07-05T03:24:04Z"},{"alias_kind":"pith_short_8","alias_value":"GVYEH25P","created_at":"2026-07-05T03:24:04Z"}],"graph_snapshots":[{"event_id":"sha256:bae216b9c189c33c7ff2ee63c9fa86bb1dcea5a376b389c990488d18081321c1","target":"graph","created_at":"2026-07-05T03:24:04Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2110.10133/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reinforcement learning (RL) algorithms can be used to provide personalized services, which rely on users' private and sensitive data. To protect the users' privacy, privacy-preserving RL algorithms are in demand. In this paper, we study RL with linear function approximation and local differential privacy (LDP) guarantees. We propose a novel $(\\varepsilon, \\delta)$-LDP algorithm for learning a class of Markov decision processes (MDPs) dubbed linear mixture MDPs, and obtains an $\\tilde{\\mathcal{O}}( d^{5/4}H^{7/4}T^{3/4}\\left(\\log(1/\\delta)\\right)^{1/4}\\sqrt{1/\\varepsilon})$ regret, where $d$ is","authors_text":"Chonghua Liao, Jiafan He, Quanquan Gu","cross_cats":["cs.CR","math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-19T17:44:09Z","title":"Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.10133","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:ce6983bd230989f02df9c3bd3406460a3ce0f58249f8a4937461f8a4b56d60f0","target":"record","created_at":"2026-07-05T03:24:04Z","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":"ea5f66241d274e00cc9564789ad419b10720c5c87067897b6a13fbe9ffc6c94e","cross_cats_sorted":["cs.CR","math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-19T17:44:09Z","title_canon_sha256":"22e2e178f0a46a7fe1ad7ef793948d9c816c9d9978f38ff9195d69cdef5b0295"},"schema_version":"1.0","source":{"id":"2110.10133","kind":"arxiv","version":1}},"canonical_sha256":"357043ebaf14d54d4ece613d8e89d0ed160d26ce276fbc87d4ff138a35b2abd9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"357043ebaf14d54d4ece613d8e89d0ed160d26ce276fbc87d4ff138a35b2abd9","first_computed_at":"2026-07-05T03:24:04.290880Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:24:04.290880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xRkVTb404warRgWSTDa+EYXJEz+wY/WdEDsBb9Mx8bJ360rNi/Kaqe/M4ucnrgWdDANfL6sd5/G4zklaw6emBw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:24:04.291324Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.10133","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ce6983bd230989f02df9c3bd3406460a3ce0f58249f8a4937461f8a4b56d60f0","sha256:bae216b9c189c33c7ff2ee63c9fa86bb1dcea5a376b389c990488d18081321c1"],"state_sha256":"125af8dce2dd90c5067ec1268c9cd30e09cae50ec3a997ba1f6511f3f2c2f151"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MK7ejG3aAxt+LRkPZIiw7hsGpmxzCOtg0kS9ll6Mv9jaL1v8DBVi8NZpKB74Y+4tqe+X1t+eDbFonR/PqPbWBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T08:49:26.115608Z","bundle_sha256":"557bc5d45089e78c19c9b5e6fbb85432a1fe455e74087af022124c4c35c75283"}}