{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:2V7BP6HQLIOKQEPLPZQC5RTHVH","short_pith_number":"pith:2V7BP6HQ","schema_version":"1.0","canonical_sha256":"d57e17f8f05a1ca811eb7e602ec667a9ffe6d6d543ee10b7d4f1867b0c7d2d78","source":{"kind":"arxiv","id":"1510.00633","version":1},"attestation_state":"computed","paper":{"title":"Distributed Multitask Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jialei Wang, Mladen Kolar, Nathan Srebro","submitted_at":"2015-10-02T16:15:30Z","abstract_excerpt":"We consider the problem of distributed multi-task learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space,where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with the optimal centralized method."},"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":"1510.00633","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-02T16:15:30Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8043f86852ad7cc59a274e71803f651bb8ec5d0f78e1b806c63cf567a34f1f0e","abstract_canon_sha256":"4e077cdd12557ef575ba154f4d685df32f2e3d2e1b68291d92ec9f87ea482d20"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:31:12.461966Z","signature_b64":"bsoBvmjg++9BGdWDdX65WS1WgMtlTjNlnOGUkKPkpUXGvQhdRO7FZGNQDUZSzR+zSKXzEkacQq5JzQCu+LwjBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d57e17f8f05a1ca811eb7e602ec667a9ffe6d6d543ee10b7d4f1867b0c7d2d78","last_reissued_at":"2026-05-18T01:31:12.461448Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:31:12.461448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distributed Multitask Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Jialei Wang, Mladen Kolar, Nathan Srebro","submitted_at":"2015-10-02T16:15:30Z","abstract_excerpt":"We consider the problem of distributed multi-task learning, where each machine learns a separate, but related, task. Specifically, each machine learns a linear predictor in high-dimensional space,where all tasks share the same small support. We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with the optimal centralized method."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.00633","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":"1510.00633","created_at":"2026-05-18T01:31:12.461513+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.00633v1","created_at":"2026-05-18T01:31:12.461513+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.00633","created_at":"2026-05-18T01:31:12.461513+00:00"},{"alias_kind":"pith_short_12","alias_value":"2V7BP6HQLIOK","created_at":"2026-05-18T12:29:02.477457+00:00"},{"alias_kind":"pith_short_16","alias_value":"2V7BP6HQLIOKQEPL","created_at":"2026-05-18T12:29:02.477457+00:00"},{"alias_kind":"pith_short_8","alias_value":"2V7BP6HQ","created_at":"2026-05-18T12:29:02.477457+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/2V7BP6HQLIOKQEPLPZQC5RTHVH","json":"https://pith.science/pith/2V7BP6HQLIOKQEPLPZQC5RTHVH.json","graph_json":"https://pith.science/api/pith-number/2V7BP6HQLIOKQEPLPZQC5RTHVH/graph.json","events_json":"https://pith.science/api/pith-number/2V7BP6HQLIOKQEPLPZQC5RTHVH/events.json","paper":"https://pith.science/paper/2V7BP6HQ"},"agent_actions":{"view_html":"https://pith.science/pith/2V7BP6HQLIOKQEPLPZQC5RTHVH","download_json":"https://pith.science/pith/2V7BP6HQLIOKQEPLPZQC5RTHVH.json","view_paper":"https://pith.science/paper/2V7BP6HQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.00633&json=true","fetch_graph":"https://pith.science/api/pith-number/2V7BP6HQLIOKQEPLPZQC5RTHVH/graph.json","fetch_events":"https://pith.science/api/pith-number/2V7BP6HQLIOKQEPLPZQC5RTHVH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2V7BP6HQLIOKQEPLPZQC5RTHVH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2V7BP6HQLIOKQEPLPZQC5RTHVH/action/storage_attestation","attest_author":"https://pith.science/pith/2V7BP6HQLIOKQEPLPZQC5RTHVH/action/author_attestation","sign_citation":"https://pith.science/pith/2V7BP6HQLIOKQEPLPZQC5RTHVH/action/citation_signature","submit_replication":"https://pith.science/pith/2V7BP6HQLIOKQEPLPZQC5RTHVH/action/replication_record"}},"created_at":"2026-05-18T01:31:12.461513+00:00","updated_at":"2026-05-18T01:31:12.461513+00:00"}