{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:B4WK3RJ35SZBYSRSK6FPFAQWDL","short_pith_number":"pith:B4WK3RJ3","schema_version":"1.0","canonical_sha256":"0f2cadc53becb21c4a32578af282161ada9da2dff205760d5a824596d538c5a7","source":{"kind":"arxiv","id":"1404.1530","version":3},"attestation_state":"computed","paper":{"title":"Provable Deterministic Leverage Score Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NA","math.IT","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.DS","authors_text":"Anastasios Kyrillidis, Christos Boutsidis, Dimitris Papailiopoulos","submitted_at":"2014-04-06T00:08:54Z","abstract_excerpt":"We explain theoretically a curious empirical phenomenon: \"Approximating a matrix by deterministically selecting a subset of its columns with the corresponding largest leverage scores results in a good low-rank matrix surrogate\". To obtain provable guarantees, previous work requires randomized sampling of the columns with probabilities proportional to their leverage scores.\n  In this work, we provide a novel theoretical analysis of deterministic leverage score sampling. We show that such deterministic sampling can be provably as accurate as its randomized counterparts, if the leverage scores fo"},"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":"1404.1530","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2014-04-06T00:08:54Z","cross_cats_sorted":["cs.IT","cs.NA","math.IT","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"1123921c1a5a5f4dd33e3b95c545e027fe3ca41a962a07747d956c072611ddfa","abstract_canon_sha256":"467a454e8f24e20677e028c11f84a519a9367263c48a4041fc66dc085b5055fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:50:33.582822Z","signature_b64":"lLuauTKTTXPJDiHG3SJIy9PbhQyWdAMZSiZ+xG0OrT45WaCC9PRfrp2OgJitHWWSgvGZ4eS+Z0AMXZP0zjagBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f2cadc53becb21c4a32578af282161ada9da2dff205760d5a824596d538c5a7","last_reissued_at":"2026-05-18T02:50:33.582414Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:50:33.582414Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Provable Deterministic Leverage Score Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NA","math.IT","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.DS","authors_text":"Anastasios Kyrillidis, Christos Boutsidis, Dimitris Papailiopoulos","submitted_at":"2014-04-06T00:08:54Z","abstract_excerpt":"We explain theoretically a curious empirical phenomenon: \"Approximating a matrix by deterministically selecting a subset of its columns with the corresponding largest leverage scores results in a good low-rank matrix surrogate\". To obtain provable guarantees, previous work requires randomized sampling of the columns with probabilities proportional to their leverage scores.\n  In this work, we provide a novel theoretical analysis of deterministic leverage score sampling. We show that such deterministic sampling can be provably as accurate as its randomized counterparts, if the leverage scores fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.1530","kind":"arxiv","version":3},"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":"1404.1530","created_at":"2026-05-18T02:50:33.582476+00:00"},{"alias_kind":"arxiv_version","alias_value":"1404.1530v3","created_at":"2026-05-18T02:50:33.582476+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.1530","created_at":"2026-05-18T02:50:33.582476+00:00"},{"alias_kind":"pith_short_12","alias_value":"B4WK3RJ35SZB","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_16","alias_value":"B4WK3RJ35SZBYSRS","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_8","alias_value":"B4WK3RJ3","created_at":"2026-05-18T12:28:19.803747+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/B4WK3RJ35SZBYSRSK6FPFAQWDL","json":"https://pith.science/pith/B4WK3RJ35SZBYSRSK6FPFAQWDL.json","graph_json":"https://pith.science/api/pith-number/B4WK3RJ35SZBYSRSK6FPFAQWDL/graph.json","events_json":"https://pith.science/api/pith-number/B4WK3RJ35SZBYSRSK6FPFAQWDL/events.json","paper":"https://pith.science/paper/B4WK3RJ3"},"agent_actions":{"view_html":"https://pith.science/pith/B4WK3RJ35SZBYSRSK6FPFAQWDL","download_json":"https://pith.science/pith/B4WK3RJ35SZBYSRSK6FPFAQWDL.json","view_paper":"https://pith.science/paper/B4WK3RJ3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1404.1530&json=true","fetch_graph":"https://pith.science/api/pith-number/B4WK3RJ35SZBYSRSK6FPFAQWDL/graph.json","fetch_events":"https://pith.science/api/pith-number/B4WK3RJ35SZBYSRSK6FPFAQWDL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B4WK3RJ35SZBYSRSK6FPFAQWDL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B4WK3RJ35SZBYSRSK6FPFAQWDL/action/storage_attestation","attest_author":"https://pith.science/pith/B4WK3RJ35SZBYSRSK6FPFAQWDL/action/author_attestation","sign_citation":"https://pith.science/pith/B4WK3RJ35SZBYSRSK6FPFAQWDL/action/citation_signature","submit_replication":"https://pith.science/pith/B4WK3RJ35SZBYSRSK6FPFAQWDL/action/replication_record"}},"created_at":"2026-05-18T02:50:33.582476+00:00","updated_at":"2026-05-18T02:50:33.582476+00:00"}