{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:6U2WAGELQGYF6QBO6SAVVOS4V4","short_pith_number":"pith:6U2WAGEL","schema_version":"1.0","canonical_sha256":"f53560188b81b05f402ef4815aba5caf253c04f628013dd90841f35ac36120f3","source":{"kind":"arxiv","id":"1301.2725","version":1},"attestation_state":"computed","paper":{"title":"Robust High Dimensional Sparse Regression and Matching Pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Constantine Caramanis, Shie Mannor, Yudong Chen","submitted_at":"2013-01-12T22:39:56Z","abstract_excerpt":"We consider high dimensional sparse regression, and develop strategies able to deal with arbitrary -- possibly, severe or coordinated -- errors in the covariance matrix $X$. These may come from corrupted data, persistent experimental errors, or malicious respondents in surveys/recommender systems, etc. Such non-stochastic error-in-variables problems are notoriously difficult to treat, and as we demonstrate, the problem is particularly pronounced in high-dimensional settings where the primary goal is {\\em support recovery} of the sparse regressor. We develop algorithms for support recovery in s"},"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":"1301.2725","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-01-12T22:39:56Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT","math.ST","stat.TH"],"title_canon_sha256":"0b3621c936c8ff96b7ccb4f21410daa6fd7cc81cda2344fe8966344f9d0b2796","abstract_canon_sha256":"834b4164b37327dce5499e4c007a93756057e640b4ad23fa14bb75a371d8852d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:36:35.433683Z","signature_b64":"qXBWIN1lM9VVIITjdVMOa9HkaEI1RgG1fggOxGIIAwGgXCTBd7JvL2XIie3u34Ha8qDKkdNK72uMOLfW7COCDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f53560188b81b05f402ef4815aba5caf253c04f628013dd90841f35ac36120f3","last_reissued_at":"2026-05-18T03:36:35.432928Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:36:35.432928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust High Dimensional Sparse Regression and Matching Pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Constantine Caramanis, Shie Mannor, Yudong Chen","submitted_at":"2013-01-12T22:39:56Z","abstract_excerpt":"We consider high dimensional sparse regression, and develop strategies able to deal with arbitrary -- possibly, severe or coordinated -- errors in the covariance matrix $X$. These may come from corrupted data, persistent experimental errors, or malicious respondents in surveys/recommender systems, etc. Such non-stochastic error-in-variables problems are notoriously difficult to treat, and as we demonstrate, the problem is particularly pronounced in high-dimensional settings where the primary goal is {\\em support recovery} of the sparse regressor. We develop algorithms for support recovery in s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.2725","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":"1301.2725","created_at":"2026-05-18T03:36:35.433052+00:00"},{"alias_kind":"arxiv_version","alias_value":"1301.2725v1","created_at":"2026-05-18T03:36:35.433052+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.2725","created_at":"2026-05-18T03:36:35.433052+00:00"},{"alias_kind":"pith_short_12","alias_value":"6U2WAGELQGYF","created_at":"2026-05-18T12:27:36.564083+00:00"},{"alias_kind":"pith_short_16","alias_value":"6U2WAGELQGYF6QBO","created_at":"2026-05-18T12:27:36.564083+00:00"},{"alias_kind":"pith_short_8","alias_value":"6U2WAGEL","created_at":"2026-05-18T12:27:36.564083+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1712.05526","citing_title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6U2WAGELQGYF6QBO6SAVVOS4V4","json":"https://pith.science/pith/6U2WAGELQGYF6QBO6SAVVOS4V4.json","graph_json":"https://pith.science/api/pith-number/6U2WAGELQGYF6QBO6SAVVOS4V4/graph.json","events_json":"https://pith.science/api/pith-number/6U2WAGELQGYF6QBO6SAVVOS4V4/events.json","paper":"https://pith.science/paper/6U2WAGEL"},"agent_actions":{"view_html":"https://pith.science/pith/6U2WAGELQGYF6QBO6SAVVOS4V4","download_json":"https://pith.science/pith/6U2WAGELQGYF6QBO6SAVVOS4V4.json","view_paper":"https://pith.science/paper/6U2WAGEL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1301.2725&json=true","fetch_graph":"https://pith.science/api/pith-number/6U2WAGELQGYF6QBO6SAVVOS4V4/graph.json","fetch_events":"https://pith.science/api/pith-number/6U2WAGELQGYF6QBO6SAVVOS4V4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6U2WAGELQGYF6QBO6SAVVOS4V4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6U2WAGELQGYF6QBO6SAVVOS4V4/action/storage_attestation","attest_author":"https://pith.science/pith/6U2WAGELQGYF6QBO6SAVVOS4V4/action/author_attestation","sign_citation":"https://pith.science/pith/6U2WAGELQGYF6QBO6SAVVOS4V4/action/citation_signature","submit_replication":"https://pith.science/pith/6U2WAGELQGYF6QBO6SAVVOS4V4/action/replication_record"}},"created_at":"2026-05-18T03:36:35.433052+00:00","updated_at":"2026-05-18T03:36:35.433052+00:00"}