{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:WTG4LBLQNPSOTTYWXK47NRCOZB","short_pith_number":"pith:WTG4LBLQ","canonical_record":{"source":{"id":"1507.00066","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-30T23:30:28Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"d804a9ee6c59c68b6ec7e07312b6f62d723ebda14b67676bdce2b0df5307114b","abstract_canon_sha256":"da7fb14497a2df81fddf6c3c08c625dda4e2fef0a8bd5934e4107ccbced39363"},"schema_version":"1.0"},"canonical_sha256":"b4cdc585706be4e9cf16bab9f6c44ec87b8f59b47dc8a315bf9c075cd2292476","source":{"kind":"arxiv","id":"1507.00066","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.00066","created_at":"2026-05-18T01:37:30Z"},{"alias_kind":"arxiv_version","alias_value":"1507.00066v1","created_at":"2026-05-18T01:37:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.00066","created_at":"2026-05-18T01:37:30Z"},{"alias_kind":"pith_short_12","alias_value":"WTG4LBLQNPSO","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_16","alias_value":"WTG4LBLQNPSOTTYW","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_8","alias_value":"WTG4LBLQ","created_at":"2026-05-18T12:29:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:WTG4LBLQNPSOTTYWXK47NRCOZB","target":"record","payload":{"canonical_record":{"source":{"id":"1507.00066","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-30T23:30:28Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"d804a9ee6c59c68b6ec7e07312b6f62d723ebda14b67676bdce2b0df5307114b","abstract_canon_sha256":"da7fb14497a2df81fddf6c3c08c625dda4e2fef0a8bd5934e4107ccbced39363"},"schema_version":"1.0"},"canonical_sha256":"b4cdc585706be4e9cf16bab9f6c44ec87b8f59b47dc8a315bf9c075cd2292476","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:30.694880Z","signature_b64":"dKG5NZbHn0LGAc4YSKe1KIdDzbI+srBoUCn7RqbU3rIzYP23K2PFU/1L8AWbOIaaGKD+nkpS3M8cP3D3CO0xBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4cdc585706be4e9cf16bab9f6c44ec87b8f59b47dc8a315bf9c075cd2292476","last_reissued_at":"2026-05-18T01:37:30.694147Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:30.694147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1507.00066","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":"2026-05-18T01:37:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o5DDEVv+bT5XW0quGpL2if9CGDJnqOVUD0NtzLgxpf3ECycqcj+SpNA54DRJ/GW56CeGSnprdZotlCz8XpG0Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T21:18:05.647968Z"},"content_sha256":"2d46e3d83a3f2cad6d1176a2920e0496e624ec2a9baacb2dfe023a97d326f88c","schema_version":"1.0","event_id":"sha256:2d46e3d83a3f2cad6d1176a2920e0496e624ec2a9baacb2dfe023a97d326f88c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:WTG4LBLQNPSOTTYWXK47NRCOZB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Cross-Validation for Incremental Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Andr\\'as Gy\\\"orgy, Csaba Szepesv\\'ari, Pooria Joulani","submitted_at":"2015-06-30T23:30:28Z","abstract_excerpt":"Cross-validation (CV) is one of the main tools for performance estimation and parameter tuning in machine learning.   The general recipe for computing CV estimate   is to run a learning algorithm separately   for each CV fold, a computationally expensive process.   In this paper, we propose a new approach to reduce   the computational burden of CV-based performance estimation.   As opposed to all previous attempts, which are specific to a particular   learning model or problem domain, we propose a general method applicable   to a large class of incremental learning algorithms,   which are uniq"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.00066","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":""},"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":"2026-05-18T01:37:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TRLPfIRSvDWWjVryMzbMeUQIgi2dxeOeaKMFR9Z94bg+Gx/9IN7p/E/qeLbPj9obqjaNRQXeDVp54MDNIx/HCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T21:18:05.648668Z"},"content_sha256":"d6d96d72550b7559653799481eda60203b458ba81900b67d4353d455e224a2a1","schema_version":"1.0","event_id":"sha256:d6d96d72550b7559653799481eda60203b458ba81900b67d4353d455e224a2a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WTG4LBLQNPSOTTYWXK47NRCOZB/bundle.json","state_url":"https://pith.science/pith/WTG4LBLQNPSOTTYWXK47NRCOZB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WTG4LBLQNPSOTTYWXK47NRCOZB/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":"2026-06-06T21:18:05Z","links":{"resolver":"https://pith.science/pith/WTG4LBLQNPSOTTYWXK47NRCOZB","bundle":"https://pith.science/pith/WTG4LBLQNPSOTTYWXK47NRCOZB/bundle.json","state":"https://pith.science/pith/WTG4LBLQNPSOTTYWXK47NRCOZB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WTG4LBLQNPSOTTYWXK47NRCOZB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:WTG4LBLQNPSOTTYWXK47NRCOZB","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":"da7fb14497a2df81fddf6c3c08c625dda4e2fef0a8bd5934e4107ccbced39363","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-30T23:30:28Z","title_canon_sha256":"d804a9ee6c59c68b6ec7e07312b6f62d723ebda14b67676bdce2b0df5307114b"},"schema_version":"1.0","source":{"id":"1507.00066","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.00066","created_at":"2026-05-18T01:37:30Z"},{"alias_kind":"arxiv_version","alias_value":"1507.00066v1","created_at":"2026-05-18T01:37:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.00066","created_at":"2026-05-18T01:37:30Z"},{"alias_kind":"pith_short_12","alias_value":"WTG4LBLQNPSO","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_16","alias_value":"WTG4LBLQNPSOTTYW","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_8","alias_value":"WTG4LBLQ","created_at":"2026-05-18T12:29:47Z"}],"graph_snapshots":[{"event_id":"sha256:d6d96d72550b7559653799481eda60203b458ba81900b67d4353d455e224a2a1","target":"graph","created_at":"2026-05-18T01:37:30Z","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"},"paper":{"abstract_excerpt":"Cross-validation (CV) is one of the main tools for performance estimation and parameter tuning in machine learning.   The general recipe for computing CV estimate   is to run a learning algorithm separately   for each CV fold, a computationally expensive process.   In this paper, we propose a new approach to reduce   the computational burden of CV-based performance estimation.   As opposed to all previous attempts, which are specific to a particular   learning model or problem domain, we propose a general method applicable   to a large class of incremental learning algorithms,   which are uniq","authors_text":"Andr\\'as Gy\\\"orgy, Csaba Szepesv\\'ari, Pooria Joulani","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-30T23:30:28Z","title":"Fast Cross-Validation for Incremental Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.00066","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":"sha256:2d46e3d83a3f2cad6d1176a2920e0496e624ec2a9baacb2dfe023a97d326f88c","target":"record","created_at":"2026-05-18T01:37:30Z","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":"da7fb14497a2df81fddf6c3c08c625dda4e2fef0a8bd5934e4107ccbced39363","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-06-30T23:30:28Z","title_canon_sha256":"d804a9ee6c59c68b6ec7e07312b6f62d723ebda14b67676bdce2b0df5307114b"},"schema_version":"1.0","source":{"id":"1507.00066","kind":"arxiv","version":1}},"canonical_sha256":"b4cdc585706be4e9cf16bab9f6c44ec87b8f59b47dc8a315bf9c075cd2292476","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b4cdc585706be4e9cf16bab9f6c44ec87b8f59b47dc8a315bf9c075cd2292476","first_computed_at":"2026-05-18T01:37:30.694147Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:37:30.694147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dKG5NZbHn0LGAc4YSKe1KIdDzbI+srBoUCn7RqbU3rIzYP23K2PFU/1L8AWbOIaaGKD+nkpS3M8cP3D3CO0xBg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:37:30.694880Z","signed_message":"canonical_sha256_bytes"},"source_id":"1507.00066","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2d46e3d83a3f2cad6d1176a2920e0496e624ec2a9baacb2dfe023a97d326f88c","sha256:d6d96d72550b7559653799481eda60203b458ba81900b67d4353d455e224a2a1"],"state_sha256":"cef67c02b57693069843e31fd4e3f4e28a392fddd7ef144b9c946b611822d3f1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V3Sn2LZSBMhMAtfAFc4n/kguKAxpVTRbA+uDAQ55M55HlHXxjehQTr7JqvQMdf2LivYcoXzCw5iGLm+I9EltDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T21:18:05.652138Z","bundle_sha256":"82cf8c8abdf393526e9f6d6056c63327e317d3b910736dc6216b4b6a06ea6183"}}