{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:35BL3AV4RIBSAAFWOYSRH7SS5R","short_pith_number":"pith:35BL3AV4","canonical_record":{"source":{"id":"1908.05571","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2019-08-15T15:01:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8d5d0d14a4c7542bd7cc8476dfbb2800c03ae5eaee522bc2d85aff2aeedf88d0","abstract_canon_sha256":"6eddf8d53f1ab4d60905cd0ebbc0d9f980943b06f34006994ac157c172ef2d72"},"schema_version":"1.0"},"canonical_sha256":"df42bd82bc8a032000b6762513fe52ec54f88c94112ff2c870f72c92b57a3d5a","source":{"kind":"arxiv","id":"1908.05571","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.05571","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"arxiv_version","alias_value":"1908.05571v1","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.05571","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"pith_short_12","alias_value":"35BL3AV4RIBS","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"pith_short_16","alias_value":"35BL3AV4RIBSAAFW","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"pith_short_8","alias_value":"35BL3AV4","created_at":"2026-07-04T23:56:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:35BL3AV4RIBSAAFWOYSRH7SS5R","target":"record","payload":{"canonical_record":{"source":{"id":"1908.05571","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2019-08-15T15:01:55Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8d5d0d14a4c7542bd7cc8476dfbb2800c03ae5eaee522bc2d85aff2aeedf88d0","abstract_canon_sha256":"6eddf8d53f1ab4d60905cd0ebbc0d9f980943b06f34006994ac157c172ef2d72"},"schema_version":"1.0"},"canonical_sha256":"df42bd82bc8a032000b6762513fe52ec54f88c94112ff2c870f72c92b57a3d5a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:56:53.976818Z","signature_b64":"FBJkCmakJI7WBkUR1qQMqdUUXjcHHuokaVGjkEHJ+PslYIY3zFRya0GTbgFjy7vJAfZskRftGMUlR3qK1jH1Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df42bd82bc8a032000b6762513fe52ec54f88c94112ff2c870f72c92b57a3d5a","last_reissued_at":"2026-07-04T23:56:53.976436Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:56:53.976436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.05571","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":"2026-07-04T23:56:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"unESHp5wZW0t7duYuKjnlifiBgZJngtK7clKh8F8+Qw8kCV/YXCAEQbj4jPZe60T7T+aaAhx7XMWRmQigdkAAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T06:08:46.164559Z"},"content_sha256":"8516cb9d060fb5d817f2c78443dee9bca3b87aeff44bb18d5cabd28b19bc99f8","schema_version":"1.0","event_id":"sha256:8516cb9d060fb5d817f2c78443dee9bca3b87aeff44bb18d5cabd28b19bc99f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:35BL3AV4RIBSAAFWOYSRH7SS5R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Lars Carlsson, Niharika Gauraha, Ola Spjuth, Robin Carri\\'on Br\\\"annstr\\\"om","submitted_at":"2019-08-15T15:01:55Z","abstract_excerpt":"Conformal Prediction is a framework that produces prediction intervals based on the output from a machine learning algorithm. In this paper we explore the case when training data is made up of multiple parts available in different sources that cannot be pooled. We here consider the regression case and propose a method where a conformal predictor is trained on each data source independently, and where the prediction intervals are then combined into a single interval. We call the approach Non-Disclosed Conformal Prediction (NDCP), and we evaluate it on a regression dataset from the UCI machine l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.05571","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.05571/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-04T23:56:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bdS/dTREbAXvequf2xlIfjtmuolSSqSM1/z7YVdkjmYm7sbXaS2aYsYONmF0fzF6bmVRddzExh2O2gmllMsLBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T06:08:46.165083Z"},"content_sha256":"36b2837dfd405831f517666c65466aab2db61590571c7bb6a9c67b7414ad84ce","schema_version":"1.0","event_id":"sha256:36b2837dfd405831f517666c65466aab2db61590571c7bb6a9c67b7414ad84ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/35BL3AV4RIBSAAFWOYSRH7SS5R/bundle.json","state_url":"https://pith.science/pith/35BL3AV4RIBSAAFWOYSRH7SS5R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/35BL3AV4RIBSAAFWOYSRH7SS5R/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":"2026-08-16T06:08:46Z","links":{"resolver":"https://pith.science/pith/35BL3AV4RIBSAAFWOYSRH7SS5R","bundle":"https://pith.science/pith/35BL3AV4RIBSAAFWOYSRH7SS5R/bundle.json","state":"https://pith.science/pith/35BL3AV4RIBSAAFWOYSRH7SS5R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/35BL3AV4RIBSAAFWOYSRH7SS5R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:35BL3AV4RIBSAAFWOYSRH7SS5R","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":"6eddf8d53f1ab4d60905cd0ebbc0d9f980943b06f34006994ac157c172ef2d72","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2019-08-15T15:01:55Z","title_canon_sha256":"8d5d0d14a4c7542bd7cc8476dfbb2800c03ae5eaee522bc2d85aff2aeedf88d0"},"schema_version":"1.0","source":{"id":"1908.05571","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.05571","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"arxiv_version","alias_value":"1908.05571v1","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.05571","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"pith_short_12","alias_value":"35BL3AV4RIBS","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"pith_short_16","alias_value":"35BL3AV4RIBSAAFW","created_at":"2026-07-04T23:56:53Z"},{"alias_kind":"pith_short_8","alias_value":"35BL3AV4","created_at":"2026-07-04T23:56:53Z"}],"graph_snapshots":[{"event_id":"sha256:36b2837dfd405831f517666c65466aab2db61590571c7bb6a9c67b7414ad84ce","target":"graph","created_at":"2026-07-04T23:56:53Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1908.05571/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Conformal Prediction is a framework that produces prediction intervals based on the output from a machine learning algorithm. In this paper we explore the case when training data is made up of multiple parts available in different sources that cannot be pooled. We here consider the regression case and propose a method where a conformal predictor is trained on each data source independently, and where the prediction intervals are then combined into a single interval. We call the approach Non-Disclosed Conformal Prediction (NDCP), and we evaluate it on a regression dataset from the UCI machine l","authors_text":"Lars Carlsson, Niharika Gauraha, Ola Spjuth, Robin Carri\\'on Br\\\"annstr\\\"om","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2019-08-15T15:01:55Z","title":"Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.05571","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":"sha256:8516cb9d060fb5d817f2c78443dee9bca3b87aeff44bb18d5cabd28b19bc99f8","target":"record","created_at":"2026-07-04T23:56:53Z","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":"6eddf8d53f1ab4d60905cd0ebbc0d9f980943b06f34006994ac157c172ef2d72","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2019-08-15T15:01:55Z","title_canon_sha256":"8d5d0d14a4c7542bd7cc8476dfbb2800c03ae5eaee522bc2d85aff2aeedf88d0"},"schema_version":"1.0","source":{"id":"1908.05571","kind":"arxiv","version":1}},"canonical_sha256":"df42bd82bc8a032000b6762513fe52ec54f88c94112ff2c870f72c92b57a3d5a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df42bd82bc8a032000b6762513fe52ec54f88c94112ff2c870f72c92b57a3d5a","first_computed_at":"2026-07-04T23:56:53.976436Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:56:53.976436Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FBJkCmakJI7WBkUR1qQMqdUUXjcHHuokaVGjkEHJ+PslYIY3zFRya0GTbgFjy7vJAfZskRftGMUlR3qK1jH1Dw==","signature_status":"signed_v1","signed_at":"2026-07-04T23:56:53.976818Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.05571","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8516cb9d060fb5d817f2c78443dee9bca3b87aeff44bb18d5cabd28b19bc99f8","sha256:36b2837dfd405831f517666c65466aab2db61590571c7bb6a9c67b7414ad84ce"],"state_sha256":"27a9f0ad72a144c0b7c5d951a789ebd1c06a40b7e2cb7c651936b450e21de8c3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IsGM1hOhs9Or2x9P7u9Nbk8hczfIuzctCWAvftBS8L2ImWVLAehOMJBgDBOmlmQzvmjyep8VnKruC12q5RekBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T06:08:46.169255Z","bundle_sha256":"42f418fb54f52f3faa8ceca81df02d17ee4d7a5d2c0f2de6143214e99676f460"}}