{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:AJRIN5GUQIGRUQGW2YLVIHOMUL","short_pith_number":"pith:AJRIN5GU","canonical_record":{"source":{"id":"1409.2638","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-09-09T08:43:14Z","cross_cats_sorted":[],"title_canon_sha256":"eccd3fb624c3be09097b27aeebc8aff53283b50be6badedc3233fa9657e39df0","abstract_canon_sha256":"bae9f94ced7d2d4806bbd122c90a94828371284c0862820beaf4c5c28baa430b"},"schema_version":"1.0"},"canonical_sha256":"026286f4d4820d1a40d6d617541dcca2ede1c30726189aa342f136f6f9145046","source":{"kind":"arxiv","id":"1409.2638","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.2638","created_at":"2026-05-18T02:43:10Z"},{"alias_kind":"arxiv_version","alias_value":"1409.2638v1","created_at":"2026-05-18T02:43:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.2638","created_at":"2026-05-18T02:43:10Z"},{"alias_kind":"pith_short_12","alias_value":"AJRIN5GUQIGR","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_16","alias_value":"AJRIN5GUQIGRUQGW","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_8","alias_value":"AJRIN5GU","created_at":"2026-05-18T12:28:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:AJRIN5GUQIGRUQGW2YLVIHOMUL","target":"record","payload":{"canonical_record":{"source":{"id":"1409.2638","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-09-09T08:43:14Z","cross_cats_sorted":[],"title_canon_sha256":"eccd3fb624c3be09097b27aeebc8aff53283b50be6badedc3233fa9657e39df0","abstract_canon_sha256":"bae9f94ced7d2d4806bbd122c90a94828371284c0862820beaf4c5c28baa430b"},"schema_version":"1.0"},"canonical_sha256":"026286f4d4820d1a40d6d617541dcca2ede1c30726189aa342f136f6f9145046","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:43:10.077398Z","signature_b64":"ln4DN+sU3jeob0l1/+MGSzcso7y5KRscN9l0jm9+T3OlH4qelrEGdkPpss1fx2NNJU7txd7Be/JYjRIG60JJCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"026286f4d4820d1a40d6d617541dcca2ede1c30726189aa342f136f6f9145046","last_reissued_at":"2026-05-18T02:43:10.076887Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:43:10.076887Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.2638","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":"2026-05-18T02:43:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D/ULVZIcfgTLhqn2kp//fY4s3gOj+iX9LkOJ8701GFA/Cbp4uR7QSJYe6q51djMG6fAk26XVxw8WBHBSRdmhDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T11:44:10.618482Z"},"content_sha256":"a4e607c4ecd59365e769bbb7b7336192aebf552a89c0ccc095605665dd0c4ca0","schema_version":"1.0","event_id":"sha256:a4e607c4ecd59365e769bbb7b7336192aebf552a89c0ccc095605665dd0c4ca0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:AJRIN5GUQIGRUQGW2YLVIHOMUL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Magging: maximin aggregation for inhomogeneous large-scale data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Nicolai Meinshausen, Peter B\\\"uhlmann","submitted_at":"2014-09-09T08:43:14Z","abstract_excerpt":"Large-scale data analysis poses both statistical and computational problems which need to be addressed simultaneously. A solution is often straightforward if the data are homogeneous: one can use classical ideas of subsampling and mean aggregation to get a computationally efficient solution with acceptable statistical accuracy, where the aggregation step simply averages the results obtained on distinct subsets of the data. However, if the data exhibit inhomogeneities (and typically they do), the same approach will be inadequate, as it will be unduly influenced by effects that are not persisten"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.2638","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":"2026-05-18T02:43:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AExKzK7gluLuAUrWS71NTLGm26UO05Ks+izI3mqNiT5qRRuz1+vDFbQPI8qchopk1HNDe1dr3XxbRHKXFeSTAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T11:44:10.618831Z"},"content_sha256":"4edfc0df6daeb61f301b38942989d34c2af4d653c86f0cbc63d9b3cc7f9eb1cc","schema_version":"1.0","event_id":"sha256:4edfc0df6daeb61f301b38942989d34c2af4d653c86f0cbc63d9b3cc7f9eb1cc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AJRIN5GUQIGRUQGW2YLVIHOMUL/bundle.json","state_url":"https://pith.science/pith/AJRIN5GUQIGRUQGW2YLVIHOMUL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AJRIN5GUQIGRUQGW2YLVIHOMUL/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":"2026-05-31T11:44:10Z","links":{"resolver":"https://pith.science/pith/AJRIN5GUQIGRUQGW2YLVIHOMUL","bundle":"https://pith.science/pith/AJRIN5GUQIGRUQGW2YLVIHOMUL/bundle.json","state":"https://pith.science/pith/AJRIN5GUQIGRUQGW2YLVIHOMUL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AJRIN5GUQIGRUQGW2YLVIHOMUL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:AJRIN5GUQIGRUQGW2YLVIHOMUL","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":"bae9f94ced7d2d4806bbd122c90a94828371284c0862820beaf4c5c28baa430b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-09-09T08:43:14Z","title_canon_sha256":"eccd3fb624c3be09097b27aeebc8aff53283b50be6badedc3233fa9657e39df0"},"schema_version":"1.0","source":{"id":"1409.2638","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.2638","created_at":"2026-05-18T02:43:10Z"},{"alias_kind":"arxiv_version","alias_value":"1409.2638v1","created_at":"2026-05-18T02:43:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.2638","created_at":"2026-05-18T02:43:10Z"},{"alias_kind":"pith_short_12","alias_value":"AJRIN5GUQIGR","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_16","alias_value":"AJRIN5GUQIGRUQGW","created_at":"2026-05-18T12:28:19Z"},{"alias_kind":"pith_short_8","alias_value":"AJRIN5GU","created_at":"2026-05-18T12:28:19Z"}],"graph_snapshots":[{"event_id":"sha256:4edfc0df6daeb61f301b38942989d34c2af4d653c86f0cbc63d9b3cc7f9eb1cc","target":"graph","created_at":"2026-05-18T02:43:10Z","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":"Large-scale data analysis poses both statistical and computational problems which need to be addressed simultaneously. A solution is often straightforward if the data are homogeneous: one can use classical ideas of subsampling and mean aggregation to get a computationally efficient solution with acceptable statistical accuracy, where the aggregation step simply averages the results obtained on distinct subsets of the data. However, if the data exhibit inhomogeneities (and typically they do), the same approach will be inadequate, as it will be unduly influenced by effects that are not persisten","authors_text":"Nicolai Meinshausen, Peter B\\\"uhlmann","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-09-09T08:43:14Z","title":"Magging: maximin aggregation for inhomogeneous large-scale data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.2638","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:a4e607c4ecd59365e769bbb7b7336192aebf552a89c0ccc095605665dd0c4ca0","target":"record","created_at":"2026-05-18T02:43:10Z","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":"bae9f94ced7d2d4806bbd122c90a94828371284c0862820beaf4c5c28baa430b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-09-09T08:43:14Z","title_canon_sha256":"eccd3fb624c3be09097b27aeebc8aff53283b50be6badedc3233fa9657e39df0"},"schema_version":"1.0","source":{"id":"1409.2638","kind":"arxiv","version":1}},"canonical_sha256":"026286f4d4820d1a40d6d617541dcca2ede1c30726189aa342f136f6f9145046","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"026286f4d4820d1a40d6d617541dcca2ede1c30726189aa342f136f6f9145046","first_computed_at":"2026-05-18T02:43:10.076887Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:43:10.076887Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ln4DN+sU3jeob0l1/+MGSzcso7y5KRscN9l0jm9+T3OlH4qelrEGdkPpss1fx2NNJU7txd7Be/JYjRIG60JJCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:43:10.077398Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.2638","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a4e607c4ecd59365e769bbb7b7336192aebf552a89c0ccc095605665dd0c4ca0","sha256:4edfc0df6daeb61f301b38942989d34c2af4d653c86f0cbc63d9b3cc7f9eb1cc"],"state_sha256":"ba9ec56e1a33edff80b9027aa97ef36b0a3615526be18065f4bfeccb9cf3940c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2apkEOc8AUXY6s4yRJO0XUBGzGtqBlsnCYr7ybM6gKSItUn3s0MMw5gU/KT7sPJDd65Zy13EfNjUTmpwGRGbDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T11:44:10.621349Z","bundle_sha256":"1a720f0c15e8c9054ab7cbaec7ce67f6defe44091fa7f62e66b3e5dd93a58d8c"}}