{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:W5SQNUETEATQ3WVVGPD7YVZL55","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":"999b923ab325e7be1094feb28215adc303cb576bd149f63466d886b88cd03c26","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-14T08:51:04Z","title_canon_sha256":"94387a2499fca38c62278d9bd39a9bf34c741e1a353aee3cedbedf0ba2624373"},"schema_version":"1.0","source":{"id":"2211.07206","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.07206","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"arxiv_version","alias_value":"2211.07206v3","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07206","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"pith_short_12","alias_value":"W5SQNUETEATQ","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"pith_short_16","alias_value":"W5SQNUETEATQ3WVV","created_at":"2026-07-05T07:27:23Z"},{"alias_kind":"pith_short_8","alias_value":"W5SQNUET","created_at":"2026-07-05T07:27:23Z"}],"graph_snapshots":[{"event_id":"sha256:4b86d9c27050440d1d65868c8cee05ee3bc57b7a889b5db595a0a37c4781dea2","target":"graph","created_at":"2026-07-05T07:27:23Z","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/2211.07206/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks. While, in practice, the number of related tasks available is often small, most of the existing approaches assume an abundance of tasks; making them unrealistic and prone to overfitting. A central question in the meta-learning literature is how to regularize to ensure generalization to unseen tasks. In this work, we provide a theoretical analysis using the PAC-Bayesian theory and present a generalization bound for meta-learning, which was first derived b","authors_text":"Andreas Krause, Jonas Rothfuss, Martin Josifoski, Vincent Fortuin","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-14T08:51:04Z","title":"Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07206","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":"sha256:37e6872f68fe994ea0e24bfd026cb04a19006164502ac89391fc8cd263b453f8","target":"record","created_at":"2026-07-05T07:27:23Z","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":"999b923ab325e7be1094feb28215adc303cb576bd149f63466d886b88cd03c26","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2022-11-14T08:51:04Z","title_canon_sha256":"94387a2499fca38c62278d9bd39a9bf34c741e1a353aee3cedbedf0ba2624373"},"schema_version":"1.0","source":{"id":"2211.07206","kind":"arxiv","version":3}},"canonical_sha256":"b76506d09320270ddab533c7fc572bef6521c647389df0d69f57889254167179","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b76506d09320270ddab533c7fc572bef6521c647389df0d69f57889254167179","first_computed_at":"2026-07-05T07:27:23.171750Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:27:23.171750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/jJTqd+zsyMW8mshXHDnENha2KbgHN1L/9SSATiYktDhtD1Na/HTq38Jn97Z2yJzdPPBjugqz59AbXy8PGqsDA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:27:23.172266Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.07206","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:37e6872f68fe994ea0e24bfd026cb04a19006164502ac89391fc8cd263b453f8","sha256:4b86d9c27050440d1d65868c8cee05ee3bc57b7a889b5db595a0a37c4781dea2"],"state_sha256":"d03d5230b39b97cb606f2f0c616af0541fe62ca51473641d9a976f5f454b99e0"}