{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:MPGE2UHTTK7D6P47666QSVPCRS","short_pith_number":"pith:MPGE2UHT","canonical_record":{"source":{"id":"1703.02965","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-08T18:58:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6ef45c59cec84737dc9ac38dbb64ac9a201e256519ec280d6e7eb0b41884050a","abstract_canon_sha256":"75cb03ee84b371866c77c0f55775c8eaedd0dd45d92ebba503f4b1b92039cd62"},"schema_version":"1.0"},"canonical_sha256":"63cc4d50f39abe3f3f9ff7bd0955e28c930ea1ce8f1668a4c495da4df5d9036c","source":{"kind":"arxiv","id":"1703.02965","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.02965","created_at":"2026-05-18T00:49:05Z"},{"alias_kind":"arxiv_version","alias_value":"1703.02965v1","created_at":"2026-05-18T00:49:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02965","created_at":"2026-05-18T00:49:05Z"},{"alias_kind":"pith_short_12","alias_value":"MPGE2UHTTK7D","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"MPGE2UHTTK7D6P47","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"MPGE2UHT","created_at":"2026-05-18T12:31:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:MPGE2UHTTK7D6P47666QSVPCRS","target":"record","payload":{"canonical_record":{"source":{"id":"1703.02965","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-08T18:58:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6ef45c59cec84737dc9ac38dbb64ac9a201e256519ec280d6e7eb0b41884050a","abstract_canon_sha256":"75cb03ee84b371866c77c0f55775c8eaedd0dd45d92ebba503f4b1b92039cd62"},"schema_version":"1.0"},"canonical_sha256":"63cc4d50f39abe3f3f9ff7bd0955e28c930ea1ce8f1668a4c495da4df5d9036c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:05.184772Z","signature_b64":"TOO/Uct1j7wXXyIeQ/Wuj1DyAa4AzJOGi5dur25or7wueLvS3UsSkXbz6lM+rT0+UI016znAe2WX9uVmnohqAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63cc4d50f39abe3f3f9ff7bd0955e28c930ea1ce8f1668a4c495da4df5d9036c","last_reissued_at":"2026-05-18T00:49:05.184339Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:05.184339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.02965","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":"2026-05-18T00:49:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GoyNaljuCf1KIZOzhb1zAHxsUhVSDYUmD0pb2Ziq+PvnfywOOXhy5HSy+JtjdJo6xvb5iKxpphLpUE7vNpwSAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T09:47:27.631719Z"},"content_sha256":"976fd5fb25b1216e150d0ac34ef27095bfd5a60aa6d67d6e01ab4ba493945ed3","schema_version":"1.0","event_id":"sha256:976fd5fb25b1216e150d0ac34ef27095bfd5a60aa6d67d6e01ab4ba493945ed3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:MPGE2UHTTK7D6P47666QSVPCRS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unsupervised Ensemble Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Boaz Nadler, Erhan Bilal, Omer Dror, Yuval Kluger","submitted_at":"2017-03-08T18:58:20Z","abstract_excerpt":"Consider a regression problem where there is no labeled data and the only observations are the predictions $f_i(x_j)$ of $m$ experts $f_{i}$ over many samples $x_j$. With no knowledge on the accuracy of the experts, is it still possible to accurately estimate the unknown responses $y_{j}$? Can one still detect the least or most accurate experts? In this work we propose a framework to study these questions, based on the assumption that the $m$ experts have uncorrelated deviations from the optimal predictor. Assuming the first two moments of the response are known, we develop methods to detect t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02965","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":"2026-05-18T00:49:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kzV5hQRZpNwgHVARvh6F0m2GH5AWtEnTfq133dP9IDN+eLF9YyhNXEyBSDDijLAuJUBggqo5vCeHKdvmmcQHAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T09:47:27.632093Z"},"content_sha256":"9dc59fdac95ce513ecc2434166c6453ba77b2941f3d9fc2ada82f5d3cbfb813e","schema_version":"1.0","event_id":"sha256:9dc59fdac95ce513ecc2434166c6453ba77b2941f3d9fc2ada82f5d3cbfb813e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MPGE2UHTTK7D6P47666QSVPCRS/bundle.json","state_url":"https://pith.science/pith/MPGE2UHTTK7D6P47666QSVPCRS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MPGE2UHTTK7D6P47666QSVPCRS/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":"2026-07-04T09:47:27Z","links":{"resolver":"https://pith.science/pith/MPGE2UHTTK7D6P47666QSVPCRS","bundle":"https://pith.science/pith/MPGE2UHTTK7D6P47666QSVPCRS/bundle.json","state":"https://pith.science/pith/MPGE2UHTTK7D6P47666QSVPCRS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MPGE2UHTTK7D6P47666QSVPCRS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:MPGE2UHTTK7D6P47666QSVPCRS","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":"75cb03ee84b371866c77c0f55775c8eaedd0dd45d92ebba503f4b1b92039cd62","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-08T18:58:20Z","title_canon_sha256":"6ef45c59cec84737dc9ac38dbb64ac9a201e256519ec280d6e7eb0b41884050a"},"schema_version":"1.0","source":{"id":"1703.02965","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.02965","created_at":"2026-05-18T00:49:05Z"},{"alias_kind":"arxiv_version","alias_value":"1703.02965v1","created_at":"2026-05-18T00:49:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02965","created_at":"2026-05-18T00:49:05Z"},{"alias_kind":"pith_short_12","alias_value":"MPGE2UHTTK7D","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"MPGE2UHTTK7D6P47","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"MPGE2UHT","created_at":"2026-05-18T12:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:9dc59fdac95ce513ecc2434166c6453ba77b2941f3d9fc2ada82f5d3cbfb813e","target":"graph","created_at":"2026-05-18T00:49:05Z","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":"Consider a regression problem where there is no labeled data and the only observations are the predictions $f_i(x_j)$ of $m$ experts $f_{i}$ over many samples $x_j$. With no knowledge on the accuracy of the experts, is it still possible to accurately estimate the unknown responses $y_{j}$? Can one still detect the least or most accurate experts? In this work we propose a framework to study these questions, based on the assumption that the $m$ experts have uncorrelated deviations from the optimal predictor. Assuming the first two moments of the response are known, we develop methods to detect t","authors_text":"Boaz Nadler, Erhan Bilal, Omer Dror, Yuval Kluger","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-08T18:58:20Z","title":"Unsupervised Ensemble Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02965","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":"sha256:976fd5fb25b1216e150d0ac34ef27095bfd5a60aa6d67d6e01ab4ba493945ed3","target":"record","created_at":"2026-05-18T00:49:05Z","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":"75cb03ee84b371866c77c0f55775c8eaedd0dd45d92ebba503f4b1b92039cd62","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-08T18:58:20Z","title_canon_sha256":"6ef45c59cec84737dc9ac38dbb64ac9a201e256519ec280d6e7eb0b41884050a"},"schema_version":"1.0","source":{"id":"1703.02965","kind":"arxiv","version":1}},"canonical_sha256":"63cc4d50f39abe3f3f9ff7bd0955e28c930ea1ce8f1668a4c495da4df5d9036c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"63cc4d50f39abe3f3f9ff7bd0955e28c930ea1ce8f1668a4c495da4df5d9036c","first_computed_at":"2026-05-18T00:49:05.184339Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:49:05.184339Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TOO/Uct1j7wXXyIeQ/Wuj1DyAa4AzJOGi5dur25or7wueLvS3UsSkXbz6lM+rT0+UI016znAe2WX9uVmnohqAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:49:05.184772Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.02965","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:976fd5fb25b1216e150d0ac34ef27095bfd5a60aa6d67d6e01ab4ba493945ed3","sha256:9dc59fdac95ce513ecc2434166c6453ba77b2941f3d9fc2ada82f5d3cbfb813e"],"state_sha256":"f0863cdb69f962cb993301da41ee791e71f19fe158ed9dbd392ac61ecc1fede8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uOVRaSE3AdrsilioaThtjObeSZ56s5LNRb+a7oIYpnSg9ibWFhUto2O+u32qCcBAuRpkMhQoVQf0e7VqUj2YCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T09:47:27.633964Z","bundle_sha256":"bfee812600bc9a77a5cb754bea611117d61245f2fe8d9ef669badb3f0f6f3ec0"}}