{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WAF5VC5GKPA6AFPS535SUWGGJ5","short_pith_number":"pith:WAF5VC5G","canonical_record":{"source":{"id":"1809.06019","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-09-17T04:53:46Z","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"title_canon_sha256":"c92cae34a6e3b5ec4f59ba0ecb24a3d195a54b92116a8f1b85704fbe07475312","abstract_canon_sha256":"2976baca15e6dc7d35d088514ac35d4cea081349d3b133f0448803d0c403f34e"},"schema_version":"1.0"},"canonical_sha256":"b00bda8ba653c1e015f2eefb2a58c64f6665ac11d5d916de9dc25816e2778499","source":{"kind":"arxiv","id":"1809.06019","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06019","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06019v1","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06019","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"pith_short_12","alias_value":"WAF5VC5GKPA6","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"WAF5VC5GKPA6AFPS","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"WAF5VC5G","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WAF5VC5GKPA6AFPS535SUWGGJ5","target":"record","payload":{"canonical_record":{"source":{"id":"1809.06019","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-09-17T04:53:46Z","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"title_canon_sha256":"c92cae34a6e3b5ec4f59ba0ecb24a3d195a54b92116a8f1b85704fbe07475312","abstract_canon_sha256":"2976baca15e6dc7d35d088514ac35d4cea081349d3b133f0448803d0c403f34e"},"schema_version":"1.0"},"canonical_sha256":"b00bda8ba653c1e015f2eefb2a58c64f6665ac11d5d916de9dc25816e2778499","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:35.850944Z","signature_b64":"LsG3VepGozCVitrQUD0NEiaKVRAPlJ/BpdfagB/x/xedspFcwj743ravM88qCGbRtQG8JBP+M7eWwBXmG0WmBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b00bda8ba653c1e015f2eefb2a58c64f6665ac11d5d916de9dc25816e2778499","last_reissued_at":"2026-05-18T00:05:35.850438Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:35.850438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.06019","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":"2026-05-18T00:05:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZKjP5Lmx24nOXtf0F/G82MvQRKDftfMNEzWirOeVFkw1uGvkeQUnfbxVgQhwlXvjMLNc6Z1DFid2f7KLb3FVAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T09:22:20.871859Z"},"content_sha256":"e968bab3b467833904d496133651c0198e9dc23b40a778d42fdf76bd50c3abe7","schema_version":"1.0","event_id":"sha256:e968bab3b467833904d496133651c0198e9dc23b40a778d42fdf76bd50c3abe7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WAF5VC5GKPA6AFPS535SUWGGJ5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Guang Cheng, Jean Honorio, Meimei Liu","submitted_at":"2018-09-17T04:53:46Z","abstract_excerpt":"In this paper, we propose a random projection approach to estimate variance in kernel ridge regression. Our approach leads to a consistent estimator of the true variance, while being computationally more efficient. Our variance estimator is optimal for a large family of kernels, including cubic splines and Gaussian kernels. Simulation analysis is conducted to support our theory."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06019","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":"2026-05-18T00:05:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7+J1MTY65tc9aoImL1JydijaodQbE3kFqgP5uMha96RXGYdVX2obvnXi529HEZYzrDStLLi8xA6yvlLm/S9bAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T09:22:20.872525Z"},"content_sha256":"1cf7931fbb55f8b306d7f2049d048cdd0eb97908934038c6103c0f9465dc871d","schema_version":"1.0","event_id":"sha256:1cf7931fbb55f8b306d7f2049d048cdd0eb97908934038c6103c0f9465dc871d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WAF5VC5GKPA6AFPS535SUWGGJ5/bundle.json","state_url":"https://pith.science/pith/WAF5VC5GKPA6AFPS535SUWGGJ5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WAF5VC5GKPA6AFPS535SUWGGJ5/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":"2026-05-25T09:22:20Z","links":{"resolver":"https://pith.science/pith/WAF5VC5GKPA6AFPS535SUWGGJ5","bundle":"https://pith.science/pith/WAF5VC5GKPA6AFPS535SUWGGJ5/bundle.json","state":"https://pith.science/pith/WAF5VC5GKPA6AFPS535SUWGGJ5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WAF5VC5GKPA6AFPS535SUWGGJ5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WAF5VC5GKPA6AFPS535SUWGGJ5","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":"2976baca15e6dc7d35d088514ac35d4cea081349d3b133f0448803d0c403f34e","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-09-17T04:53:46Z","title_canon_sha256":"c92cae34a6e3b5ec4f59ba0ecb24a3d195a54b92116a8f1b85704fbe07475312"},"schema_version":"1.0","source":{"id":"1809.06019","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.06019","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"arxiv_version","alias_value":"1809.06019v1","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.06019","created_at":"2026-05-18T00:05:35Z"},{"alias_kind":"pith_short_12","alias_value":"WAF5VC5GKPA6","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"WAF5VC5GKPA6AFPS","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"WAF5VC5G","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:1cf7931fbb55f8b306d7f2049d048cdd0eb97908934038c6103c0f9465dc871d","target":"graph","created_at":"2026-05-18T00:05:35Z","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":"In this paper, we propose a random projection approach to estimate variance in kernel ridge regression. Our approach leads to a consistent estimator of the true variance, while being computationally more efficient. Our variance estimator is optimal for a large family of kernels, including cubic splines and Gaussian kernels. Simulation analysis is conducted to support our theory.","authors_text":"Guang Cheng, Jean Honorio, Meimei Liu","cross_cats":["cs.LG","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-09-17T04:53:46Z","title":"Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.06019","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:e968bab3b467833904d496133651c0198e9dc23b40a778d42fdf76bd50c3abe7","target":"record","created_at":"2026-05-18T00:05:35Z","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":"2976baca15e6dc7d35d088514ac35d4cea081349d3b133f0448803d0c403f34e","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-09-17T04:53:46Z","title_canon_sha256":"c92cae34a6e3b5ec4f59ba0ecb24a3d195a54b92116a8f1b85704fbe07475312"},"schema_version":"1.0","source":{"id":"1809.06019","kind":"arxiv","version":1}},"canonical_sha256":"b00bda8ba653c1e015f2eefb2a58c64f6665ac11d5d916de9dc25816e2778499","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b00bda8ba653c1e015f2eefb2a58c64f6665ac11d5d916de9dc25816e2778499","first_computed_at":"2026-05-18T00:05:35.850438Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:35.850438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LsG3VepGozCVitrQUD0NEiaKVRAPlJ/BpdfagB/x/xedspFcwj743ravM88qCGbRtQG8JBP+M7eWwBXmG0WmBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:35.850944Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.06019","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e968bab3b467833904d496133651c0198e9dc23b40a778d42fdf76bd50c3abe7","sha256:1cf7931fbb55f8b306d7f2049d048cdd0eb97908934038c6103c0f9465dc871d"],"state_sha256":"f6dc7d8afa658e2e994724ee2a914c30f54b4b27c9e8930dcbb293a9a40b02bf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"usEkyPeIvt0aWBCngCrsAYvKwcYCM48uxyW3Y6JlTga+FYOxMdYe71386xhA6/wEluz6pQ2lYWrKQ8tq6afmBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T09:22:20.875008Z","bundle_sha256":"24d980de56a8eefd3e82eba88f7c50a08257c9a226faa8ab881bc133cde24b77"}}