{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:L36DWW4COLNCW6O3KORUM2E2AX","short_pith_number":"pith:L36DWW4C","schema_version":"1.0","canonical_sha256":"5efc3b5b8272da2b79db53a346689a05e864fdc6db0823235a41311b0b91e9ba","source":{"kind":"arxiv","id":"1406.0304","version":1},"attestation_state":"computed","paper":{"title":"Transductive Learning for Multi-Task Copula Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fabio Ramos, Markus Schneider","submitted_at":"2014-06-02T09:22:49Z","abstract_excerpt":"We tackle the problem of multi-task learning with copula process. Multivariable prediction in spatial and spatial-temporal processes such as natural resource estimation and pollution monitoring have been typically addressed using techniques based on Gaussian processes and co-Kriging. While the Gaussian prior assumption is convenient from analytical and computational perspectives, nature is dominated by non-Gaussian likelihoods. Copula processes are an elegant and flexible solution to handle various non-Gaussian likelihoods by capturing the dependence structure of random variables with cumulati"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1406.0304","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-06-02T09:22:49Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"99b4231d9696546cd877d3ac224216314f08a1ce3280c687f59475c241b02404","abstract_canon_sha256":"7f4ca47b0366f40d2d16b09d976ce16547835d15daa18f8459476f055b0656fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:50:41.215690Z","signature_b64":"j6snsVdVEfwYGfNYCnXUMMcQ5JXwqKFgjRbaRW4B/AyhqjZk5CB23iVcuLnm5celYlpcYzlnUjen4fNBx06IBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5efc3b5b8272da2b79db53a346689a05e864fdc6db0823235a41311b0b91e9ba","last_reissued_at":"2026-05-18T02:50:41.215185Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:50:41.215185Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Transductive Learning for Multi-Task Copula Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fabio Ramos, Markus Schneider","submitted_at":"2014-06-02T09:22:49Z","abstract_excerpt":"We tackle the problem of multi-task learning with copula process. Multivariable prediction in spatial and spatial-temporal processes such as natural resource estimation and pollution monitoring have been typically addressed using techniques based on Gaussian processes and co-Kriging. While the Gaussian prior assumption is convenient from analytical and computational perspectives, nature is dominated by non-Gaussian likelihoods. Copula processes are an elegant and flexible solution to handle various non-Gaussian likelihoods by capturing the dependence structure of random variables with cumulati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.0304","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1406.0304","created_at":"2026-05-18T02:50:41.215252+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.0304v1","created_at":"2026-05-18T02:50:41.215252+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.0304","created_at":"2026-05-18T02:50:41.215252+00:00"},{"alias_kind":"pith_short_12","alias_value":"L36DWW4COLNC","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_16","alias_value":"L36DWW4COLNCW6O3","created_at":"2026-05-18T12:28:35.611951+00:00"},{"alias_kind":"pith_short_8","alias_value":"L36DWW4C","created_at":"2026-05-18T12:28:35.611951+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L36DWW4COLNCW6O3KORUM2E2AX","json":"https://pith.science/pith/L36DWW4COLNCW6O3KORUM2E2AX.json","graph_json":"https://pith.science/api/pith-number/L36DWW4COLNCW6O3KORUM2E2AX/graph.json","events_json":"https://pith.science/api/pith-number/L36DWW4COLNCW6O3KORUM2E2AX/events.json","paper":"https://pith.science/paper/L36DWW4C"},"agent_actions":{"view_html":"https://pith.science/pith/L36DWW4COLNCW6O3KORUM2E2AX","download_json":"https://pith.science/pith/L36DWW4COLNCW6O3KORUM2E2AX.json","view_paper":"https://pith.science/paper/L36DWW4C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.0304&json=true","fetch_graph":"https://pith.science/api/pith-number/L36DWW4COLNCW6O3KORUM2E2AX/graph.json","fetch_events":"https://pith.science/api/pith-number/L36DWW4COLNCW6O3KORUM2E2AX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L36DWW4COLNCW6O3KORUM2E2AX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L36DWW4COLNCW6O3KORUM2E2AX/action/storage_attestation","attest_author":"https://pith.science/pith/L36DWW4COLNCW6O3KORUM2E2AX/action/author_attestation","sign_citation":"https://pith.science/pith/L36DWW4COLNCW6O3KORUM2E2AX/action/citation_signature","submit_replication":"https://pith.science/pith/L36DWW4COLNCW6O3KORUM2E2AX/action/replication_record"}},"created_at":"2026-05-18T02:50:41.215252+00:00","updated_at":"2026-05-18T02:50:41.215252+00:00"}