{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:BYBJN64IZOUZ6RH25LEPQE5IGD","short_pith_number":"pith:BYBJN64I","canonical_record":{"source":{"id":"2410.13577","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-17T14:12:35Z","cross_cats_sorted":[],"title_canon_sha256":"741674118799b466f68f58c424e865d6af5a451312b6c62071a73176f88477f9","abstract_canon_sha256":"e7a16f2d32346de857c5829beb89474c8c7e66bcedbc06d8ebb55695397c7ef0"},"schema_version":"1.0"},"canonical_sha256":"0e0296fb88cba99f44faeac8f813a830ee6dce5f7c6d75594f5658753472fab6","source":{"kind":"arxiv","id":"2410.13577","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13577","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13577v3","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13577","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"pith_short_12","alias_value":"BYBJN64IZOUZ","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"pith_short_16","alias_value":"BYBJN64IZOUZ6RH2","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"pith_short_8","alias_value":"BYBJN64I","created_at":"2026-07-05T11:16:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:BYBJN64IZOUZ6RH25LEPQE5IGD","target":"record","payload":{"canonical_record":{"source":{"id":"2410.13577","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-17T14:12:35Z","cross_cats_sorted":[],"title_canon_sha256":"741674118799b466f68f58c424e865d6af5a451312b6c62071a73176f88477f9","abstract_canon_sha256":"e7a16f2d32346de857c5829beb89474c8c7e66bcedbc06d8ebb55695397c7ef0"},"schema_version":"1.0"},"canonical_sha256":"0e0296fb88cba99f44faeac8f813a830ee6dce5f7c6d75594f5658753472fab6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:30.026614Z","signature_b64":"0MxiUPCOSqGQMa4n4IVJhwzF4VSOiMVoegmeQGPI0rqnvgO/XTBfKT1JVn3WxH52XwUJpUQilzAUYEyeu4NLBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e0296fb88cba99f44faeac8f813a830ee6dce5f7c6d75594f5658753472fab6","last_reissued_at":"2026-07-05T11:16:30.026048Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:30.026048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.13577","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:16:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rYTd+tMMQoZEcAY85Px0QyGRCgTmCDcDLLOyn05ILDEOcOvzIkKDX0S2d+nBp5AJcW/7wwLQ7K8SgjUvgpcyBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:09:28.248519Z"},"content_sha256":"5eef274a210aeb5c7fe17bd1013094824dab033cb4e4df37f6af275e3731a8b0","schema_version":"1.0","event_id":"sha256:5eef274a210aeb5c7fe17bd1013094824dab033cb4e4df37f6af275e3731a8b0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:BYBJN64IZOUZ6RH25LEPQE5IGD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexandre Drouin, Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours, Pascal Germain","submitted_at":"2024-10-17T14:12:35Z","abstract_excerpt":"Both PAC-Bayesian and Sample Compress learning frameworks are instrumental for deriving tight (non-vacuous) generalization bounds for neural networks. We leverage these results in a meta-learning scheme, relying on a hypernetwork that outputs the parameters of a downstream predictor from a dataset input. The originality of our approach lies in the investigated hypernetwork architectures that encode the dataset before decoding the parameters: (1) a PAC-Bayesian encoder that expresses a posterior distribution over a latent space, (2) a Sample Compress encoder that selects a small sample of the d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13577","kind":"arxiv","version":3},"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/2410.13577/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":"2026-07-05T11:16:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ETBxibeUB+nAlT3dBjhaSkytpAOKONM+idNkWIajMTiQtBjZaTpOXGkcmaTAhspsqIKnRfzLJOWU0Kry65onDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:09:28.248899Z"},"content_sha256":"5df45b783b93d11b1f3830cb3ec90bcc3ac518b20ae22bd5c60d6d176f306eb2","schema_version":"1.0","event_id":"sha256:5df45b783b93d11b1f3830cb3ec90bcc3ac518b20ae22bd5c60d6d176f306eb2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BYBJN64IZOUZ6RH25LEPQE5IGD/bundle.json","state_url":"https://pith.science/pith/BYBJN64IZOUZ6RH25LEPQE5IGD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BYBJN64IZOUZ6RH25LEPQE5IGD/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":"2026-07-07T15:09:28Z","links":{"resolver":"https://pith.science/pith/BYBJN64IZOUZ6RH25LEPQE5IGD","bundle":"https://pith.science/pith/BYBJN64IZOUZ6RH25LEPQE5IGD/bundle.json","state":"https://pith.science/pith/BYBJN64IZOUZ6RH25LEPQE5IGD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BYBJN64IZOUZ6RH25LEPQE5IGD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:BYBJN64IZOUZ6RH25LEPQE5IGD","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":"e7a16f2d32346de857c5829beb89474c8c7e66bcedbc06d8ebb55695397c7ef0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-17T14:12:35Z","title_canon_sha256":"741674118799b466f68f58c424e865d6af5a451312b6c62071a73176f88477f9"},"schema_version":"1.0","source":{"id":"2410.13577","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13577","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13577v3","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13577","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"pith_short_12","alias_value":"BYBJN64IZOUZ","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"pith_short_16","alias_value":"BYBJN64IZOUZ6RH2","created_at":"2026-07-05T11:16:30Z"},{"alias_kind":"pith_short_8","alias_value":"BYBJN64I","created_at":"2026-07-05T11:16:30Z"}],"graph_snapshots":[{"event_id":"sha256:5df45b783b93d11b1f3830cb3ec90bcc3ac518b20ae22bd5c60d6d176f306eb2","target":"graph","created_at":"2026-07-05T11:16:30Z","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/2410.13577/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Both PAC-Bayesian and Sample Compress learning frameworks are instrumental for deriving tight (non-vacuous) generalization bounds for neural networks. We leverage these results in a meta-learning scheme, relying on a hypernetwork that outputs the parameters of a downstream predictor from a dataset input. The originality of our approach lies in the investigated hypernetwork architectures that encode the dataset before decoding the parameters: (1) a PAC-Bayesian encoder that expresses a posterior distribution over a latent space, (2) a Sample Compress encoder that selects a small sample of the d","authors_text":"Alexandre Drouin, Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours, Pascal Germain","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-17T14:12:35Z","title":"Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13577","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":"sha256:5eef274a210aeb5c7fe17bd1013094824dab033cb4e4df37f6af275e3731a8b0","target":"record","created_at":"2026-07-05T11:16:30Z","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":"e7a16f2d32346de857c5829beb89474c8c7e66bcedbc06d8ebb55695397c7ef0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-17T14:12:35Z","title_canon_sha256":"741674118799b466f68f58c424e865d6af5a451312b6c62071a73176f88477f9"},"schema_version":"1.0","source":{"id":"2410.13577","kind":"arxiv","version":3}},"canonical_sha256":"0e0296fb88cba99f44faeac8f813a830ee6dce5f7c6d75594f5658753472fab6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0e0296fb88cba99f44faeac8f813a830ee6dce5f7c6d75594f5658753472fab6","first_computed_at":"2026-07-05T11:16:30.026048Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:16:30.026048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0MxiUPCOSqGQMa4n4IVJhwzF4VSOiMVoegmeQGPI0rqnvgO/XTBfKT1JVn3WxH52XwUJpUQilzAUYEyeu4NLBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:16:30.026614Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.13577","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5eef274a210aeb5c7fe17bd1013094824dab033cb4e4df37f6af275e3731a8b0","sha256:5df45b783b93d11b1f3830cb3ec90bcc3ac518b20ae22bd5c60d6d176f306eb2"],"state_sha256":"f80c4c98ce251d53f040a4aae1f1c3b5f96d5cc21164f3a0802d79393d330010"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wr+38oIXQQrLs3aozfR9h7uW8pqqnuLS4s0TBULh+rqk6owgSXHRD+6qB/m0E8ybx/D2udgKO+UUAgAX+TSvCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:09:28.251146Z","bundle_sha256":"de9e26e4a361815eb07c13a16b64f25e635ba0ab27a281c4d8011440a16b980e"}}