{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2ZDZENGUEAAMMT5N423O2VRMVE","short_pith_number":"pith:2ZDZENGU","schema_version":"1.0","canonical_sha256":"d6479234d42000c64fade6b6ed562ca91c53ca702d4890c97c6207253a92c3fd","source":{"kind":"arxiv","id":"2205.12695","version":2},"attestation_state":"computed","paper":{"title":"Surprises in adversarially-trained linear regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR","cs.LG","eess.SP","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Ant\\^onio H. Ribeiro, Dave Zachariah, Thomas B. Sch\\\"on","submitted_at":"2022-05-25T11:54:42Z","abstract_excerpt":"State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against such examples. It is formulated as a min-max problem, searching for the best solution when the training data was corrupted by the worst-case attacks. For linear regression problems, adversarial training can be formulated as a convex problem. We use this reformulation to make two technical contributions: First, we formulate the training problem as an instance of robust regression to reveal its connection "},"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":"2205.12695","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-05-25T11:54:42Z","cross_cats_sorted":["cs.CR","cs.LG","eess.SP","math.ST","stat.TH"],"title_canon_sha256":"d05be833b0b1f79831dff4c1a990e66f80c21339409b50d20c5df7d414e08210","abstract_canon_sha256":"dd438aec62933b19dc36063b8916de8ea0070af5f431fee99b9a0b488a6bcc9e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:08:28.580880Z","signature_b64":"ectQ/5BCfsBfOT3r1Zvca1Ae530r4p33vxfq21kvHZa5y9Pn6LI5UpEDygjp0MPoaJTKgIBnB2KKSLY3T/h6Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6479234d42000c64fade6b6ed562ca91c53ca702d4890c97c6207253a92c3fd","last_reissued_at":"2026-07-05T05:08:28.580399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:08:28.580399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Surprises in adversarially-trained linear regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR","cs.LG","eess.SP","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Ant\\^onio H. Ribeiro, Dave Zachariah, Thomas B. Sch\\\"on","submitted_at":"2022-05-25T11:54:42Z","abstract_excerpt":"State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against such examples. It is formulated as a min-max problem, searching for the best solution when the training data was corrupted by the worst-case attacks. For linear regression problems, adversarial training can be formulated as a convex problem. We use this reformulation to make two technical contributions: First, we formulate the training problem as an instance of robust regression to reveal its connection "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.12695","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/2205.12695/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":"2205.12695","created_at":"2026-07-05T05:08:28.580455+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.12695v2","created_at":"2026-07-05T05:08:28.580455+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.12695","created_at":"2026-07-05T05:08:28.580455+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZDZENGUEAAM","created_at":"2026-07-05T05:08:28.580455+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZDZENGUEAAMMT5N","created_at":"2026-07-05T05:08:28.580455+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZDZENGU","created_at":"2026-07-05T05:08:28.580455+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/2ZDZENGUEAAMMT5N423O2VRMVE","json":"https://pith.science/pith/2ZDZENGUEAAMMT5N423O2VRMVE.json","graph_json":"https://pith.science/api/pith-number/2ZDZENGUEAAMMT5N423O2VRMVE/graph.json","events_json":"https://pith.science/api/pith-number/2ZDZENGUEAAMMT5N423O2VRMVE/events.json","paper":"https://pith.science/paper/2ZDZENGU"},"agent_actions":{"view_html":"https://pith.science/pith/2ZDZENGUEAAMMT5N423O2VRMVE","download_json":"https://pith.science/pith/2ZDZENGUEAAMMT5N423O2VRMVE.json","view_paper":"https://pith.science/paper/2ZDZENGU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.12695&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZDZENGUEAAMMT5N423O2VRMVE/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZDZENGUEAAMMT5N423O2VRMVE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZDZENGUEAAMMT5N423O2VRMVE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZDZENGUEAAMMT5N423O2VRMVE/action/storage_attestation","attest_author":"https://pith.science/pith/2ZDZENGUEAAMMT5N423O2VRMVE/action/author_attestation","sign_citation":"https://pith.science/pith/2ZDZENGUEAAMMT5N423O2VRMVE/action/citation_signature","submit_replication":"https://pith.science/pith/2ZDZENGUEAAMMT5N423O2VRMVE/action/replication_record"}},"created_at":"2026-07-05T05:08:28.580455+00:00","updated_at":"2026-07-05T05:08:28.580455+00:00"}