{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:LUGAM7U5IQ5XZ7XOWS7UWQXWXG","short_pith_number":"pith:LUGAM7U5","canonical_record":{"source":{"id":"2110.15761","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-10-29T13:18:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"71041a38617ba3066e108b851d559e3e1d5d3194101d4f776ad6fa676e8b66a5","abstract_canon_sha256":"d88895d4aa8302fd4ff5765bb2d74bc55afa4961e8bb8ede7e52d3138424431a"},"schema_version":"1.0"},"canonical_sha256":"5d0c067e9d443b7cfeeeb4bf4b42f6b98cadc9f9c0defd6d4967dc35c5342e37","source":{"kind":"arxiv","id":"2110.15761","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.15761","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"arxiv_version","alias_value":"2110.15761v1","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.15761","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"pith_short_12","alias_value":"LUGAM7U5IQ5X","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"pith_short_16","alias_value":"LUGAM7U5IQ5XZ7XO","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"pith_short_8","alias_value":"LUGAM7U5","created_at":"2026-07-05T03:27:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:LUGAM7U5IQ5XZ7XOWS7UWQXWXG","target":"record","payload":{"canonical_record":{"source":{"id":"2110.15761","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-10-29T13:18:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"71041a38617ba3066e108b851d559e3e1d5d3194101d4f776ad6fa676e8b66a5","abstract_canon_sha256":"d88895d4aa8302fd4ff5765bb2d74bc55afa4961e8bb8ede7e52d3138424431a"},"schema_version":"1.0"},"canonical_sha256":"5d0c067e9d443b7cfeeeb4bf4b42f6b98cadc9f9c0defd6d4967dc35c5342e37","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:27:13.747295Z","signature_b64":"mbbYnL1wMZZto1R8xtzkG5jrrpV3UBqY8RNdimOl6GJdDfy7q1QEVtBlnjXKjmZLsLB6gvU8lqidnwuM04diDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d0c067e9d443b7cfeeeb4bf4b42f6b98cadc9f9c0defd6d4967dc35c5342e37","last_reissued_at":"2026-07-05T03:27:13.746842Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:27:13.746842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.15761","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":"2026-07-05T03:27:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gQI2zIkDZHQvXfYquZuHaeBsvFDpZbm7zJC31PD72wMlpNIsnQagcZ0okSUEyagGAYmowfYYHwuE4F1N7MG7BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:57:06.671546Z"},"content_sha256":"eff6a3a832544d77d41ab0d2fe3eadd7727cb509ec17577a77f108f8be479d23","schema_version":"1.0","event_id":"sha256:eff6a3a832544d77d41ab0d2fe3eadd7727cb509ec17577a77f108f8be479d23"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:LUGAM7U5IQ5XZ7XOWS7UWQXWXG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Aligned Multi-Task Gaussian Process","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Adam Hartshorne, Carl Henrik Ek, Hedvig Kjellstr\\\"om, Ieva Kazlauskaite, Neill D. F. Campbell, Olga Mikheeva","submitted_at":"2021-10-29T13:18:13Z","abstract_excerpt":"Multi-task learning requires accurate identification of the correlations between tasks. In real-world time-series, tasks are rarely perfectly temporally aligned; traditional multi-task models do not account for this and subsequent errors in correlation estimation will result in poor predictive performance and uncertainty quantification. We introduce a method that automatically accounts for temporal misalignment in a unified generative model that improves predictive performance. Our method uses Gaussian processes (GPs) to model the correlations both within and between the tasks. Building on the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.15761","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/2110.15761/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":"2026-07-05T03:27:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5+vqdcBOLwdyAOb4uYAMp3AeXjoqXW/zOt0+stCEn4rLQdeDjcuFftSrB9rzNqdro1M5xzp53wh08BbseTNlAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:57:06.672126Z"},"content_sha256":"0cca5509448c9883844cc2615e08f97f51e9a3bfcb9b0fb87657894f7a4ae0a2","schema_version":"1.0","event_id":"sha256:0cca5509448c9883844cc2615e08f97f51e9a3bfcb9b0fb87657894f7a4ae0a2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LUGAM7U5IQ5XZ7XOWS7UWQXWXG/bundle.json","state_url":"https://pith.science/pith/LUGAM7U5IQ5XZ7XOWS7UWQXWXG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LUGAM7U5IQ5XZ7XOWS7UWQXWXG/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":"2026-08-04T07:57:06Z","links":{"resolver":"https://pith.science/pith/LUGAM7U5IQ5XZ7XOWS7UWQXWXG","bundle":"https://pith.science/pith/LUGAM7U5IQ5XZ7XOWS7UWQXWXG/bundle.json","state":"https://pith.science/pith/LUGAM7U5IQ5XZ7XOWS7UWQXWXG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LUGAM7U5IQ5XZ7XOWS7UWQXWXG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:LUGAM7U5IQ5XZ7XOWS7UWQXWXG","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":"d88895d4aa8302fd4ff5765bb2d74bc55afa4961e8bb8ede7e52d3138424431a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-10-29T13:18:13Z","title_canon_sha256":"71041a38617ba3066e108b851d559e3e1d5d3194101d4f776ad6fa676e8b66a5"},"schema_version":"1.0","source":{"id":"2110.15761","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.15761","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"arxiv_version","alias_value":"2110.15761v1","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.15761","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"pith_short_12","alias_value":"LUGAM7U5IQ5X","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"pith_short_16","alias_value":"LUGAM7U5IQ5XZ7XO","created_at":"2026-07-05T03:27:13Z"},{"alias_kind":"pith_short_8","alias_value":"LUGAM7U5","created_at":"2026-07-05T03:27:13Z"}],"graph_snapshots":[{"event_id":"sha256:0cca5509448c9883844cc2615e08f97f51e9a3bfcb9b0fb87657894f7a4ae0a2","target":"graph","created_at":"2026-07-05T03:27:13Z","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/2110.15761/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-task learning requires accurate identification of the correlations between tasks. In real-world time-series, tasks are rarely perfectly temporally aligned; traditional multi-task models do not account for this and subsequent errors in correlation estimation will result in poor predictive performance and uncertainty quantification. We introduce a method that automatically accounts for temporal misalignment in a unified generative model that improves predictive performance. Our method uses Gaussian processes (GPs) to model the correlations both within and between the tasks. Building on the","authors_text":"Adam Hartshorne, Carl Henrik Ek, Hedvig Kjellstr\\\"om, Ieva Kazlauskaite, Neill D. F. Campbell, Olga Mikheeva","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-10-29T13:18:13Z","title":"Aligned Multi-Task Gaussian Process"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.15761","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:eff6a3a832544d77d41ab0d2fe3eadd7727cb509ec17577a77f108f8be479d23","target":"record","created_at":"2026-07-05T03:27:13Z","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":"d88895d4aa8302fd4ff5765bb2d74bc55afa4961e8bb8ede7e52d3138424431a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2021-10-29T13:18:13Z","title_canon_sha256":"71041a38617ba3066e108b851d559e3e1d5d3194101d4f776ad6fa676e8b66a5"},"schema_version":"1.0","source":{"id":"2110.15761","kind":"arxiv","version":1}},"canonical_sha256":"5d0c067e9d443b7cfeeeb4bf4b42f6b98cadc9f9c0defd6d4967dc35c5342e37","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d0c067e9d443b7cfeeeb4bf4b42f6b98cadc9f9c0defd6d4967dc35c5342e37","first_computed_at":"2026-07-05T03:27:13.746842Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:27:13.746842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mbbYnL1wMZZto1R8xtzkG5jrrpV3UBqY8RNdimOl6GJdDfy7q1QEVtBlnjXKjmZLsLB6gvU8lqidnwuM04diDA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:27:13.747295Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.15761","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eff6a3a832544d77d41ab0d2fe3eadd7727cb509ec17577a77f108f8be479d23","sha256:0cca5509448c9883844cc2615e08f97f51e9a3bfcb9b0fb87657894f7a4ae0a2"],"state_sha256":"c3501ea0e295f6e82da043dc9baa19a3ef789b42cf6386e3ef0bd0043f90fa31"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZvMyFPia2bUfe9osoixm/KpdMEB244QoMxQE8HWJi0cLL139yjv1nvnX2XT1hT3YAROj0u8LVRuzKuAMhka8BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T07:57:06.698466Z","bundle_sha256":"a7a418ab430c8149882931d93dc1156df4bf11e84c7fe9662a69bed18ea5ee60"}}