{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:UZN2XB4G53357RHZULXQZRYUOK","short_pith_number":"pith:UZN2XB4G","canonical_record":{"source":{"id":"1906.01684","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T19:03:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"60a93bb5d0141559646516fd3b9191431d731ebebaa2df33900a3ccbeb2f4fe2","abstract_canon_sha256":"fed3668cbad4ed0846b1ded4d2b5ec1de62b5328e905332ceafba357ac7691c8"},"schema_version":"1.0"},"canonical_sha256":"a65bab8786eef7dfc4f9a2ef0cc714729dfc54a36a214763369e61a76fe654c2","source":{"kind":"arxiv","id":"1906.01684","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01684","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01684v2","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01684","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"pith_short_12","alias_value":"UZN2XB4G5335","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"UZN2XB4G53357RHZ","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"UZN2XB4G","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:UZN2XB4G53357RHZULXQZRYUOK","target":"record","payload":{"canonical_record":{"source":{"id":"1906.01684","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T19:03:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"60a93bb5d0141559646516fd3b9191431d731ebebaa2df33900a3ccbeb2f4fe2","abstract_canon_sha256":"fed3668cbad4ed0846b1ded4d2b5ec1de62b5328e905332ceafba357ac7691c8"},"schema_version":"1.0"},"canonical_sha256":"a65bab8786eef7dfc4f9a2ef0cc714729dfc54a36a214763369e61a76fe654c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:32.512282Z","signature_b64":"5O7Fk30PAgrH3B4aAAQS1++6A+kUpZFrwZw4hne6G0dtd0KGVcJ6103wdBft8JnMezc5UmaHtGWOk5WXNC+xDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a65bab8786eef7dfc4f9a2ef0cc714729dfc54a36a214763369e61a76fe654c2","last_reissued_at":"2026-05-17T23:43:32.511900Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:32.511900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.01684","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:43:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kNGhKegGkAuQSVTSyZERp0tS+yo5isiFaU3xt+B4gW1P6tDOVpgOpWWYNjF29c5GjNK0iqKfp7cajMP7HNeDBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T18:23:52.377665Z"},"content_sha256":"64966b540416f9e06cd320757fb619bc05a550649a933c2aeb160e1f6e870c74","schema_version":"1.0","event_id":"sha256:64966b540416f9e06cd320757fb619bc05a550649a933c2aeb160e1f6e870c74"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:UZN2XB4G53357RHZULXQZRYUOK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andr\\'e Carlos Ponce de Leon Ferreira de Carvalho, Andr\\'e Luis Debiaso Rossi, Edesio Alcoba\\c{c}a, Joaquin Vanschoren, Rafael Gomes Mantovani","submitted_at":"2019-06-04T19:03:07Z","abstract_excerpt":"For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come at a high computational cost, especially on larger datasets, while the tuned settings do not always significantly outperform the default values. This paper proposes a recommender system based on meta-learning to identify exactly when it is better to use default values and when to tune hyperparameters for each new dataset. Besides, an in-depth analysis is performed to understand what they take into account for their d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01684","kind":"arxiv","version":2},"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":"2026-05-17T23:43:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h2k3o3Zd0CIgXRQjOV3PsZNMGyDziFLRuG7dT32P7etJy32sihmFopCZe0/EMgn/rgamKQsoTW1SMqvGkyQWDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T18:23:52.378392Z"},"content_sha256":"ae86f0ac76413da6d030bc683c5d7d8fe8122b928f3b86f0201d2d42fb44be02","schema_version":"1.0","event_id":"sha256:ae86f0ac76413da6d030bc683c5d7d8fe8122b928f3b86f0201d2d42fb44be02"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UZN2XB4G53357RHZULXQZRYUOK/bundle.json","state_url":"https://pith.science/pith/UZN2XB4G53357RHZULXQZRYUOK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UZN2XB4G53357RHZULXQZRYUOK/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":"2026-06-08T18:23:52Z","links":{"resolver":"https://pith.science/pith/UZN2XB4G53357RHZULXQZRYUOK","bundle":"https://pith.science/pith/UZN2XB4G53357RHZULXQZRYUOK/bundle.json","state":"https://pith.science/pith/UZN2XB4G53357RHZULXQZRYUOK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UZN2XB4G53357RHZULXQZRYUOK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:UZN2XB4G53357RHZULXQZRYUOK","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":"fed3668cbad4ed0846b1ded4d2b5ec1de62b5328e905332ceafba357ac7691c8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T19:03:07Z","title_canon_sha256":"60a93bb5d0141559646516fd3b9191431d731ebebaa2df33900a3ccbeb2f4fe2"},"schema_version":"1.0","source":{"id":"1906.01684","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01684","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01684v2","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01684","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"pith_short_12","alias_value":"UZN2XB4G5335","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"UZN2XB4G53357RHZ","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"UZN2XB4G","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:ae86f0ac76413da6d030bc683c5d7d8fe8122b928f3b86f0201d2d42fb44be02","target":"graph","created_at":"2026-05-17T23:43:32Z","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":"For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come at a high computational cost, especially on larger datasets, while the tuned settings do not always significantly outperform the default values. This paper proposes a recommender system based on meta-learning to identify exactly when it is better to use default values and when to tune hyperparameters for each new dataset. Besides, an in-depth analysis is performed to understand what they take into account for their d","authors_text":"Andr\\'e Carlos Ponce de Leon Ferreira de Carvalho, Andr\\'e Luis Debiaso Rossi, Edesio Alcoba\\c{c}a, Joaquin Vanschoren, Rafael Gomes Mantovani","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T19:03:07Z","title":"A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01684","kind":"arxiv","version":2},"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":"sha256:64966b540416f9e06cd320757fb619bc05a550649a933c2aeb160e1f6e870c74","target":"record","created_at":"2026-05-17T23:43:32Z","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":"fed3668cbad4ed0846b1ded4d2b5ec1de62b5328e905332ceafba357ac7691c8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-04T19:03:07Z","title_canon_sha256":"60a93bb5d0141559646516fd3b9191431d731ebebaa2df33900a3ccbeb2f4fe2"},"schema_version":"1.0","source":{"id":"1906.01684","kind":"arxiv","version":2}},"canonical_sha256":"a65bab8786eef7dfc4f9a2ef0cc714729dfc54a36a214763369e61a76fe654c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a65bab8786eef7dfc4f9a2ef0cc714729dfc54a36a214763369e61a76fe654c2","first_computed_at":"2026-05-17T23:43:32.511900Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:32.511900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5O7Fk30PAgrH3B4aAAQS1++6A+kUpZFrwZw4hne6G0dtd0KGVcJ6103wdBft8JnMezc5UmaHtGWOk5WXNC+xDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:32.512282Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.01684","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:64966b540416f9e06cd320757fb619bc05a550649a933c2aeb160e1f6e870c74","sha256:ae86f0ac76413da6d030bc683c5d7d8fe8122b928f3b86f0201d2d42fb44be02"],"state_sha256":"e9e6505f6f27b313f4e9a7017fa51552ae51e8b7839e81c240e78dc5feb2a118"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7C5+6hgFSwTgtH9/w5W3On2edeveHnujOvgqgFD848OnJArUFEZDxDxGUQoCO/LyZaWMtJlDgjS4YjyKO6wqAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T18:23:52.381512Z","bundle_sha256":"b20c3b81382af5aad52784827ab7abbfd12c63beaddf69011ab31d55df864477"}}