{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:2IQJEIAP4AQX44CP5TYVDTCZKB","short_pith_number":"pith:2IQJEIAP","schema_version":"1.0","canonical_sha256":"d22092200fe0217e704fecf151cc595060354f6552ba1de026ba75f7eef6b4f2","source":{"kind":"arxiv","id":"1507.05870","version":2},"attestation_state":"computed","paper":{"title":"A statistical perspective of sampling scores for linear regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Aarti Singh, Jelena Kova\\v{c}evi\\'c, Rohan Varma, Siheng Chen","submitted_at":"2015-07-21T15:25:49Z","abstract_excerpt":"In this paper, we consider a statistical problem of learning a linear model from noisy samples. Existing work has focused on approximating the least squares solution by using leverage-based scores as an importance sampling distribution. However, no finite sample statistical guarantees and no computationally efficient optimal sampling strategies have been proposed. To evaluate the statistical properties of different sampling strategies, we propose a simple yet effective estimator, which is easy for theoretical analysis and is useful in multitask linear regression. We derive the exact mean squar"},"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":"1507.05870","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-07-21T15:25:49Z","cross_cats_sorted":[],"title_canon_sha256":"861940e64446067b451ae4d91d939934652ab893c6c015545e6b06fcaa77b4cf","abstract_canon_sha256":"70c37504804dbe86e8743c1aad234bcae541db175e5815bce278028da0161c13"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:21:01.008433Z","signature_b64":"lMxa6JfvlULjaNj77ry22xVkoRPdxzhQP/wVdKSc0is1brpU0FKh8wFqRgcrt7hTqntjLY4wNh9eDUdWUGsxAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d22092200fe0217e704fecf151cc595060354f6552ba1de026ba75f7eef6b4f2","last_reissued_at":"2026-05-18T01:21:01.008005Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:21:01.008005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A statistical perspective of sampling scores for linear regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Aarti Singh, Jelena Kova\\v{c}evi\\'c, Rohan Varma, Siheng Chen","submitted_at":"2015-07-21T15:25:49Z","abstract_excerpt":"In this paper, we consider a statistical problem of learning a linear model from noisy samples. Existing work has focused on approximating the least squares solution by using leverage-based scores as an importance sampling distribution. However, no finite sample statistical guarantees and no computationally efficient optimal sampling strategies have been proposed. To evaluate the statistical properties of different sampling strategies, we propose a simple yet effective estimator, which is easy for theoretical analysis and is useful in multitask linear regression. We derive the exact mean squar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.05870","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":"1507.05870","created_at":"2026-05-18T01:21:01.008075+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.05870v2","created_at":"2026-05-18T01:21:01.008075+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.05870","created_at":"2026-05-18T01:21:01.008075+00:00"},{"alias_kind":"pith_short_12","alias_value":"2IQJEIAP4AQX","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_16","alias_value":"2IQJEIAP4AQX44CP","created_at":"2026-05-18T12:28:59.999130+00:00"},{"alias_kind":"pith_short_8","alias_value":"2IQJEIAP","created_at":"2026-05-18T12:28:59.999130+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/2IQJEIAP4AQX44CP5TYVDTCZKB","json":"https://pith.science/pith/2IQJEIAP4AQX44CP5TYVDTCZKB.json","graph_json":"https://pith.science/api/pith-number/2IQJEIAP4AQX44CP5TYVDTCZKB/graph.json","events_json":"https://pith.science/api/pith-number/2IQJEIAP4AQX44CP5TYVDTCZKB/events.json","paper":"https://pith.science/paper/2IQJEIAP"},"agent_actions":{"view_html":"https://pith.science/pith/2IQJEIAP4AQX44CP5TYVDTCZKB","download_json":"https://pith.science/pith/2IQJEIAP4AQX44CP5TYVDTCZKB.json","view_paper":"https://pith.science/paper/2IQJEIAP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.05870&json=true","fetch_graph":"https://pith.science/api/pith-number/2IQJEIAP4AQX44CP5TYVDTCZKB/graph.json","fetch_events":"https://pith.science/api/pith-number/2IQJEIAP4AQX44CP5TYVDTCZKB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2IQJEIAP4AQX44CP5TYVDTCZKB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2IQJEIAP4AQX44CP5TYVDTCZKB/action/storage_attestation","attest_author":"https://pith.science/pith/2IQJEIAP4AQX44CP5TYVDTCZKB/action/author_attestation","sign_citation":"https://pith.science/pith/2IQJEIAP4AQX44CP5TYVDTCZKB/action/citation_signature","submit_replication":"https://pith.science/pith/2IQJEIAP4AQX44CP5TYVDTCZKB/action/replication_record"}},"created_at":"2026-05-18T01:21:01.008075+00:00","updated_at":"2026-05-18T01:21:01.008075+00:00"}