{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PLVL2IF34ONZGRRL7YPBYAEAZD","short_pith_number":"pith:PLVL2IF3","schema_version":"1.0","canonical_sha256":"7aeabd20bbe39b93462bfe1e1c0080c8c5705cb68a64c20c91e7f153ec7d2813","source":{"kind":"arxiv","id":"1903.08192","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain","submitted_at":"2019-03-19T18:08:20Z","abstract_excerpt":"We study the problem of robust linear regression with response variable corruptions. We consider the oblivious adversary model, where the adversary corrupts a fraction of the responses in complete ignorance of the data. We provide a nearly linear time estimator which consistently estimates the true regression vector, even with $1-o(1)$ fraction of corruptions. Existing results in this setting either don't guarantee consistent estimates or can only handle a small fraction of corruptions. We also extend our estimator to robust sparse linear regression and show that similar guarantees hold in thi"},"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":"1903.08192","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-19T18:08:20Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f58addd65bd60866ab62c3a7f3a3e8b5bacce26440613ba744873ea4b50a8b05","abstract_canon_sha256":"2499ffff51fa7c9ed64a66f3137c17943a723e958a9fabd992f25164ac98bfce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:50.119202Z","signature_b64":"lw1skDsh9NiuOOihChG5RqI/dkZJQ7nt41x5s/uhSFmQE/PFDHz1KP1HX1fqOyEG/wwCoQx54GQ5qM2Aio5TAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7aeabd20bbe39b93462bfe1e1c0080c8c5705cb68a64c20c91e7f153ec7d2813","last_reissued_at":"2026-05-17T23:50:50.118549Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:50.118549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, Prateek Jain","submitted_at":"2019-03-19T18:08:20Z","abstract_excerpt":"We study the problem of robust linear regression with response variable corruptions. We consider the oblivious adversary model, where the adversary corrupts a fraction of the responses in complete ignorance of the data. We provide a nearly linear time estimator which consistently estimates the true regression vector, even with $1-o(1)$ fraction of corruptions. Existing results in this setting either don't guarantee consistent estimates or can only handle a small fraction of corruptions. We also extend our estimator to robust sparse linear regression and show that similar guarantees hold in thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.08192","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":"1903.08192","created_at":"2026-05-17T23:50:50.118646+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.08192v1","created_at":"2026-05-17T23:50:50.118646+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.08192","created_at":"2026-05-17T23:50:50.118646+00:00"},{"alias_kind":"pith_short_12","alias_value":"PLVL2IF34ONZ","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"PLVL2IF34ONZGRRL","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"PLVL2IF3","created_at":"2026-05-18T12:33:24.271573+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/PLVL2IF34ONZGRRL7YPBYAEAZD","json":"https://pith.science/pith/PLVL2IF34ONZGRRL7YPBYAEAZD.json","graph_json":"https://pith.science/api/pith-number/PLVL2IF34ONZGRRL7YPBYAEAZD/graph.json","events_json":"https://pith.science/api/pith-number/PLVL2IF34ONZGRRL7YPBYAEAZD/events.json","paper":"https://pith.science/paper/PLVL2IF3"},"agent_actions":{"view_html":"https://pith.science/pith/PLVL2IF34ONZGRRL7YPBYAEAZD","download_json":"https://pith.science/pith/PLVL2IF34ONZGRRL7YPBYAEAZD.json","view_paper":"https://pith.science/paper/PLVL2IF3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.08192&json=true","fetch_graph":"https://pith.science/api/pith-number/PLVL2IF34ONZGRRL7YPBYAEAZD/graph.json","fetch_events":"https://pith.science/api/pith-number/PLVL2IF34ONZGRRL7YPBYAEAZD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PLVL2IF34ONZGRRL7YPBYAEAZD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PLVL2IF34ONZGRRL7YPBYAEAZD/action/storage_attestation","attest_author":"https://pith.science/pith/PLVL2IF34ONZGRRL7YPBYAEAZD/action/author_attestation","sign_citation":"https://pith.science/pith/PLVL2IF34ONZGRRL7YPBYAEAZD/action/citation_signature","submit_replication":"https://pith.science/pith/PLVL2IF34ONZGRRL7YPBYAEAZD/action/replication_record"}},"created_at":"2026-05-17T23:50:50.118646+00:00","updated_at":"2026-05-17T23:50:50.118646+00:00"}