{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:FXQXC6CQMFUYBVBFWFEXZVNNMX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3faa0ebde2863ef5ad8e456f9943bed102c0f948d1dee36c68dd103bd46ce8af","cross_cats_sorted":["cs.CC","cs.DS","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-31T18:23:29Z","title_canon_sha256":"6ba421492a4142d1478357a8d8e930de6a51b7e83a2690550643f1d04a0068e7"},"schema_version":"1.0","source":{"id":"1806.00040","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00040","created_at":"2026-05-18T00:14:29Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00040v1","created_at":"2026-05-18T00:14:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00040","created_at":"2026-05-18T00:14:29Z"},{"alias_kind":"pith_short_12","alias_value":"FXQXC6CQMFUY","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FXQXC6CQMFUYBVBF","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FXQXC6CQ","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:c914154a1b0b11a78913375b546499b91dc98147dfe15009097d0c49d9bed9a4","target":"graph","created_at":"2026-05-18T00:14:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We study the problem of high-dimensional linear regression in a robust model where an $\\epsilon$-fraction of the samples can be adversarially corrupted. We focus on the fundamental setting where the covariates of the uncorrupted samples are drawn from a Gaussian distribution $\\mathcal{N}(0, \\Sigma)$ on $\\mathbb{R}^d$. We give nearly tight upper bounds and computational lower bounds for this problem. Specifically, our main contributions are as follows:\n  For the case that the covariance matrix is known to be the identity, we give a sample near-optimal and computationally efficient algorithm tha","authors_text":"Alistair Stewart, Ilias Diakonikolas, Weihao Kong","cross_cats":["cs.CC","cs.DS","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-31T18:23:29Z","title":"Efficient Algorithms and Lower Bounds for Robust Linear Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00040","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fcb2d4ec47b4ef3d0269b1bdb975a9ca632d6da90881e8bb83503d68dfb31eaa","target":"record","created_at":"2026-05-18T00:14:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3faa0ebde2863ef5ad8e456f9943bed102c0f948d1dee36c68dd103bd46ce8af","cross_cats_sorted":["cs.CC","cs.DS","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-31T18:23:29Z","title_canon_sha256":"6ba421492a4142d1478357a8d8e930de6a51b7e83a2690550643f1d04a0068e7"},"schema_version":"1.0","source":{"id":"1806.00040","kind":"arxiv","version":1}},"canonical_sha256":"2de1717850616980d425b1497cd5ad65d4c8aa0557aa072c674ecce556beb465","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2de1717850616980d425b1497cd5ad65d4c8aa0557aa072c674ecce556beb465","first_computed_at":"2026-05-18T00:14:29.603011Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:29.603011Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bt8vNW2Ir2AxoCtrWLz5l74NJe5syTU9qe0Nj7UHU0b/U+MwSa0AffFe0VmA6jja8Ts/bR7pWQu+y9NdnEDyCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:29.603702Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.00040","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fcb2d4ec47b4ef3d0269b1bdb975a9ca632d6da90881e8bb83503d68dfb31eaa","sha256:c914154a1b0b11a78913375b546499b91dc98147dfe15009097d0c49d9bed9a4"],"state_sha256":"405cbf1fd61ce1188cd7a44f3b6ab443cf1cfe00a965439a9a31d7e89ab68594"}