{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:J5B4DU2KWMFVE5ENIPOIGNNM3F","short_pith_number":"pith:J5B4DU2K","schema_version":"1.0","canonical_sha256":"4f43c1d34ab30b52748d43dc8335acd94b0e0713eafa47f1dbc4a9c7985c8053","source":{"kind":"arxiv","id":"2501.07046","version":1},"attestation_state":"computed","paper":{"title":"Differentially Private Kernelized Contextual Bandits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Nikola Pavlovic, Qing Zhao, Sudeep Salgia","submitted_at":"2025-01-13T04:05:19Z","abstract_excerpt":"We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space (RKHS). We study this problem under the additional constraint of joint differential privacy, where the agents needs to ensure that the sequence of query points is differentially private with respect to both the sequence of contexts and rewards. We propose a novel algorithm that improves upon the state of the art and achieves an error rate of $\\mathcal{O}\\left(\\sqrt{\\frac{\\gamma_T}{T}} + \\frac{\\gamma_T}{T \\varepsilon}\\right)$ aft"},"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":"2501.07046","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-01-13T04:05:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e05e1429506610d8a0d5f8fb0ee7659a69d85aa94cad5f6e3890b366c31c4933","abstract_canon_sha256":"c409c20d4e7c3e2191c3f26dd8b6234d81528a671dac8c85cd86e48fbc1d2bdd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:14.206354Z","signature_b64":"glM9NsLO6MF8lsK7Y9qS1KqCPI8GvXerCJCFWuqFOZ2uNceTdkbL2OUj5vl6CmUYSn0WiZk6qdVcwB+PvoG8CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f43c1d34ab30b52748d43dc8335acd94b0e0713eafa47f1dbc4a9c7985c8053","last_reissued_at":"2026-07-05T10:00:14.205752Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:14.205752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differentially Private Kernelized Contextual Bandits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Nikola Pavlovic, Qing Zhao, Sudeep Salgia","submitted_at":"2025-01-13T04:05:19Z","abstract_excerpt":"We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space (RKHS). We study this problem under the additional constraint of joint differential privacy, where the agents needs to ensure that the sequence of query points is differentially private with respect to both the sequence of contexts and rewards. We propose a novel algorithm that improves upon the state of the art and achieves an error rate of $\\mathcal{O}\\left(\\sqrt{\\frac{\\gamma_T}{T}} + \\frac{\\gamma_T}{T \\varepsilon}\\right)$ aft"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.07046","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/2501.07046/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.07046","created_at":"2026-07-05T10:00:14.205811+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.07046v1","created_at":"2026-07-05T10:00:14.205811+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.07046","created_at":"2026-07-05T10:00:14.205811+00:00"},{"alias_kind":"pith_short_12","alias_value":"J5B4DU2KWMFV","created_at":"2026-07-05T10:00:14.205811+00:00"},{"alias_kind":"pith_short_16","alias_value":"J5B4DU2KWMFVE5EN","created_at":"2026-07-05T10:00:14.205811+00:00"},{"alias_kind":"pith_short_8","alias_value":"J5B4DU2K","created_at":"2026-07-05T10:00:14.205811+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.05613","citing_title":"Optimal Regret of Bernoulli Bandits under Global Differential Privacy","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J5B4DU2KWMFVE5ENIPOIGNNM3F","json":"https://pith.science/pith/J5B4DU2KWMFVE5ENIPOIGNNM3F.json","graph_json":"https://pith.science/api/pith-number/J5B4DU2KWMFVE5ENIPOIGNNM3F/graph.json","events_json":"https://pith.science/api/pith-number/J5B4DU2KWMFVE5ENIPOIGNNM3F/events.json","paper":"https://pith.science/paper/J5B4DU2K"},"agent_actions":{"view_html":"https://pith.science/pith/J5B4DU2KWMFVE5ENIPOIGNNM3F","download_json":"https://pith.science/pith/J5B4DU2KWMFVE5ENIPOIGNNM3F.json","view_paper":"https://pith.science/paper/J5B4DU2K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.07046&json=true","fetch_graph":"https://pith.science/api/pith-number/J5B4DU2KWMFVE5ENIPOIGNNM3F/graph.json","fetch_events":"https://pith.science/api/pith-number/J5B4DU2KWMFVE5ENIPOIGNNM3F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J5B4DU2KWMFVE5ENIPOIGNNM3F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J5B4DU2KWMFVE5ENIPOIGNNM3F/action/storage_attestation","attest_author":"https://pith.science/pith/J5B4DU2KWMFVE5ENIPOIGNNM3F/action/author_attestation","sign_citation":"https://pith.science/pith/J5B4DU2KWMFVE5ENIPOIGNNM3F/action/citation_signature","submit_replication":"https://pith.science/pith/J5B4DU2KWMFVE5ENIPOIGNNM3F/action/replication_record"}},"created_at":"2026-07-05T10:00:14.205811+00:00","updated_at":"2026-07-05T10:00:14.205811+00:00"}