{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:4Z6YLYSNCWRWB3U4RD226OSDI2","short_pith_number":"pith:4Z6YLYSN","schema_version":"1.0","canonical_sha256":"e67d85e24d15a360ee9c88f5af3a43468ebfc4276fcecf4d10b6b89945a44690","source":{"kind":"arxiv","id":"2009.03986","version":2},"attestation_state":"computed","paper":{"title":"Conditional Uncorrelation and Efficient Non-approximate Subset Selection in Sparse Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fei-Yue Wang, Jianji Wang, Nanning Zheng, Qi Liu, Shupei Zhang","submitted_at":"2020-09-08T20:32:26Z","abstract_excerpt":"Given $m$ $d$-dimensional responsors and $n$ $d$-dimensional predictors, sparse regression finds at most $k$ predictors for each responsor for linear approximation, $1\\leq k \\leq d-1$. The key problem in sparse regression is subset selection, which usually suffers from high computational cost. Recent years, many improved approximate methods of subset selection have been published. However, less attention has been paid on the non-approximate method of subset selection, which is very necessary for many questions in data analysis. Here we consider sparse regression from the view of correlation, a"},"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":"2009.03986","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-09-08T20:32:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a8ea425c976dd7c8a1fd1aba98b69e76d74274ca36f8eca4c004cca961773b13","abstract_canon_sha256":"7197837e9df4412c31ccdc573c665bad45b58c3f8dc8dc8e1d4fabda1f079df4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:53:56.543754Z","signature_b64":"mTW4K1ojGSRVBuSZTQdfDhyo4fzyU5CxpdmekaJTfiSDsvPuIojIWHI8fzp12u3COFpcBWuyQXMLkKLOw+HUCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e67d85e24d15a360ee9c88f5af3a43468ebfc4276fcecf4d10b6b89945a44690","last_reissued_at":"2026-07-05T01:53:56.543208Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:53:56.543208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conditional Uncorrelation and Efficient Non-approximate Subset Selection in Sparse Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Fei-Yue Wang, Jianji Wang, Nanning Zheng, Qi Liu, Shupei Zhang","submitted_at":"2020-09-08T20:32:26Z","abstract_excerpt":"Given $m$ $d$-dimensional responsors and $n$ $d$-dimensional predictors, sparse regression finds at most $k$ predictors for each responsor for linear approximation, $1\\leq k \\leq d-1$. The key problem in sparse regression is subset selection, which usually suffers from high computational cost. Recent years, many improved approximate methods of subset selection have been published. However, less attention has been paid on the non-approximate method of subset selection, which is very necessary for many questions in data analysis. Here we consider sparse regression from the view of correlation, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.03986","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2009.03986/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2009.03986","created_at":"2026-07-05T01:53:56.543268+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.03986v2","created_at":"2026-07-05T01:53:56.543268+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.03986","created_at":"2026-07-05T01:53:56.543268+00:00"},{"alias_kind":"pith_short_12","alias_value":"4Z6YLYSNCWRW","created_at":"2026-07-05T01:53:56.543268+00:00"},{"alias_kind":"pith_short_16","alias_value":"4Z6YLYSNCWRWB3U4","created_at":"2026-07-05T01:53:56.543268+00:00"},{"alias_kind":"pith_short_8","alias_value":"4Z6YLYSN","created_at":"2026-07-05T01:53:56.543268+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/4Z6YLYSNCWRWB3U4RD226OSDI2","json":"https://pith.science/pith/4Z6YLYSNCWRWB3U4RD226OSDI2.json","graph_json":"https://pith.science/api/pith-number/4Z6YLYSNCWRWB3U4RD226OSDI2/graph.json","events_json":"https://pith.science/api/pith-number/4Z6YLYSNCWRWB3U4RD226OSDI2/events.json","paper":"https://pith.science/paper/4Z6YLYSN"},"agent_actions":{"view_html":"https://pith.science/pith/4Z6YLYSNCWRWB3U4RD226OSDI2","download_json":"https://pith.science/pith/4Z6YLYSNCWRWB3U4RD226OSDI2.json","view_paper":"https://pith.science/paper/4Z6YLYSN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.03986&json=true","fetch_graph":"https://pith.science/api/pith-number/4Z6YLYSNCWRWB3U4RD226OSDI2/graph.json","fetch_events":"https://pith.science/api/pith-number/4Z6YLYSNCWRWB3U4RD226OSDI2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4Z6YLYSNCWRWB3U4RD226OSDI2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4Z6YLYSNCWRWB3U4RD226OSDI2/action/storage_attestation","attest_author":"https://pith.science/pith/4Z6YLYSNCWRWB3U4RD226OSDI2/action/author_attestation","sign_citation":"https://pith.science/pith/4Z6YLYSNCWRWB3U4RD226OSDI2/action/citation_signature","submit_replication":"https://pith.science/pith/4Z6YLYSNCWRWB3U4RD226OSDI2/action/replication_record"}},"created_at":"2026-07-05T01:53:56.543268+00:00","updated_at":"2026-07-05T01:53:56.543268+00:00"}