{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:F4OGEURBSPMBS4L3LUQJ4UV6XF","short_pith_number":"pith:F4OGEURB","schema_version":"1.0","canonical_sha256":"2f1c62522193d819717b5d209e52beb95760bc3a0ad4baa374385f1b97f0f5a2","source":{"kind":"arxiv","id":"1504.03101","version":2},"attestation_state":"computed","paper":{"title":"Convex Learning of Multiple Tasks and their Structure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Carlo Ciliberto, Lorenzo Rosasco, Tomaso Poggio, Youssef Mroueh","submitted_at":"2015-04-13T09:13:23Z","abstract_excerpt":"Reducing the amount of human supervision is a key problem in machine learning and a natural approach is that of exploiting the relations (structure) among different tasks. This is the idea at the core of multi-task learning. In this context a fundamental question is how to incorporate the tasks structure in the learning problem.We tackle this question by studying a general computational framework that allows to encode a-priori knowledge of the tasks structure in the form of a convex penalty; in this setting a variety of previously proposed methods can be recovered as special cases, including l"},"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":"1504.03101","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-04-13T09:13:23Z","cross_cats_sorted":[],"title_canon_sha256":"5aa3801bcea22abaaa7b47bb49cd8a963ebab7f760a3370363facaa3b03bc95d","abstract_canon_sha256":"8d911879815d347844b0e79512de5ec5820c3f099a34c46a4ddc8aa3042506d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:18:30.942215Z","signature_b64":"HCqv8my5V1yVIZHrC7f5lWxt9h2hNUKAbYowTTPAaw5a2oaGAd0JtW6TVOBSxL4tjHp6I9deIuq8MFftuhkoDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f1c62522193d819717b5d209e52beb95760bc3a0ad4baa374385f1b97f0f5a2","last_reissued_at":"2026-05-18T02:18:30.941772Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:18:30.941772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Convex Learning of Multiple Tasks and their Structure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Carlo Ciliberto, Lorenzo Rosasco, Tomaso Poggio, Youssef Mroueh","submitted_at":"2015-04-13T09:13:23Z","abstract_excerpt":"Reducing the amount of human supervision is a key problem in machine learning and a natural approach is that of exploiting the relations (structure) among different tasks. This is the idea at the core of multi-task learning. In this context a fundamental question is how to incorporate the tasks structure in the learning problem.We tackle this question by studying a general computational framework that allows to encode a-priori knowledge of the tasks structure in the form of a convex penalty; in this setting a variety of previously proposed methods can be recovered as special cases, including l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.03101","kind":"arxiv","version":2},"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":"1504.03101","created_at":"2026-05-18T02:18:30.941837+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.03101v2","created_at":"2026-05-18T02:18:30.941837+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.03101","created_at":"2026-05-18T02:18:30.941837+00:00"},{"alias_kind":"pith_short_12","alias_value":"F4OGEURBSPMB","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_16","alias_value":"F4OGEURBSPMBS4L3","created_at":"2026-05-18T12:29:19.899920+00:00"},{"alias_kind":"pith_short_8","alias_value":"F4OGEURB","created_at":"2026-05-18T12:29:19.899920+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/F4OGEURBSPMBS4L3LUQJ4UV6XF","json":"https://pith.science/pith/F4OGEURBSPMBS4L3LUQJ4UV6XF.json","graph_json":"https://pith.science/api/pith-number/F4OGEURBSPMBS4L3LUQJ4UV6XF/graph.json","events_json":"https://pith.science/api/pith-number/F4OGEURBSPMBS4L3LUQJ4UV6XF/events.json","paper":"https://pith.science/paper/F4OGEURB"},"agent_actions":{"view_html":"https://pith.science/pith/F4OGEURBSPMBS4L3LUQJ4UV6XF","download_json":"https://pith.science/pith/F4OGEURBSPMBS4L3LUQJ4UV6XF.json","view_paper":"https://pith.science/paper/F4OGEURB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.03101&json=true","fetch_graph":"https://pith.science/api/pith-number/F4OGEURBSPMBS4L3LUQJ4UV6XF/graph.json","fetch_events":"https://pith.science/api/pith-number/F4OGEURBSPMBS4L3LUQJ4UV6XF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F4OGEURBSPMBS4L3LUQJ4UV6XF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F4OGEURBSPMBS4L3LUQJ4UV6XF/action/storage_attestation","attest_author":"https://pith.science/pith/F4OGEURBSPMBS4L3LUQJ4UV6XF/action/author_attestation","sign_citation":"https://pith.science/pith/F4OGEURBSPMBS4L3LUQJ4UV6XF/action/citation_signature","submit_replication":"https://pith.science/pith/F4OGEURBSPMBS4L3LUQJ4UV6XF/action/replication_record"}},"created_at":"2026-05-18T02:18:30.941837+00:00","updated_at":"2026-05-18T02:18:30.941837+00:00"}