{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:JNQVOXIVOW6NARCAOGLGXIOR52","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":"4c0bdb1ee5f9fb4fb102d1976c29f92c9d764306e51c28ec2ed4daf24c899a63","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-01T17:46:51Z","title_canon_sha256":"fc7675677fef9fcde13aecb188050711c399d5514cc3177f320d476ca6d745bf"},"schema_version":"1.0","source":{"id":"2202.00602","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.00602","created_at":"2026-07-05T04:32:38Z"},{"alias_kind":"arxiv_version","alias_value":"2202.00602v3","created_at":"2026-07-05T04:32:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.00602","created_at":"2026-07-05T04:32:38Z"},{"alias_kind":"pith_short_12","alias_value":"JNQVOXIVOW6N","created_at":"2026-07-05T04:32:38Z"},{"alias_kind":"pith_short_16","alias_value":"JNQVOXIVOW6NARCA","created_at":"2026-07-05T04:32:38Z"},{"alias_kind":"pith_short_8","alias_value":"JNQVOXIV","created_at":"2026-07-05T04:32:38Z"}],"graph_snapshots":[{"event_id":"sha256:352aafeadc078867e1cceac8af1d4f37d2a6f26ca50ea8937ee2d0c0392a9ee9","target":"graph","created_at":"2026-07-05T04:32:38Z","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/2202.00602/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Obtaining reliable, adaptive confidence sets for prediction functions (hypotheses) is a central challenge in sequential decision-making tasks, such as bandits and model-based reinforcement learning. These confidence sets typically rely on prior assumptions on the hypothesis space, e.g., the known kernel of a Reproducing Kernel Hilbert Space (RKHS). Hand-designing such kernels is error prone, and misspecification may lead to poor or unsafe performance. In this work, we propose to meta-learn a kernel from offline data (Meta-KeL). For the case where the unknown kernel is a combination of known ba","authors_text":"Andreas Krause, Jonas Rothfuss, Parnian Kassraie","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-01T17:46:51Z","title":"Meta-Learning Hypothesis Spaces for Sequential Decision-making"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.00602","kind":"arxiv","version":3},"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:ba9de31b643fd0b8251d0ad6be0cf921cd97a61ca9b8fdb6cbdc29602b3054d9","target":"record","created_at":"2026-07-05T04:32:38Z","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":"4c0bdb1ee5f9fb4fb102d1976c29f92c9d764306e51c28ec2ed4daf24c899a63","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-02-01T17:46:51Z","title_canon_sha256":"fc7675677fef9fcde13aecb188050711c399d5514cc3177f320d476ca6d745bf"},"schema_version":"1.0","source":{"id":"2202.00602","kind":"arxiv","version":3}},"canonical_sha256":"4b61575d1575bcd0444071966ba1d1ee9a45179c7dbc232cfa4b1567616ae24a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b61575d1575bcd0444071966ba1d1ee9a45179c7dbc232cfa4b1567616ae24a","first_computed_at":"2026-07-05T04:32:38.879553Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:32:38.879553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jF/qa3gkegbmj5rYCwOtNgTi+aCTdBLJfY6wRFmMx/KjocHGA1lPNHxz3Avy7Y1iqgTRb6swV1rSaTnLuD4IBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:32:38.880020Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.00602","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ba9de31b643fd0b8251d0ad6be0cf921cd97a61ca9b8fdb6cbdc29602b3054d9","sha256:352aafeadc078867e1cceac8af1d4f37d2a6f26ca50ea8937ee2d0c0392a9ee9"],"state_sha256":"ff2e4c40d9994dd32f0b9b4306118c0f300e7e4cbd1698a399a92c4e5ac920c1"}