{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:FE4CECTCMNQVY7C62FJJDIWWY3","short_pith_number":"pith:FE4CECTC","schema_version":"1.0","canonical_sha256":"2938220a6263615c7c5ed15291a2d6c6e4880ea2fc6d157f0bea7cb214de66c9","source":{"kind":"arxiv","id":"1901.00532","version":1},"attestation_state":"computed","paper":{"title":"Adversarial Robustness May Be at Odds With Simplicity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Preetum Nakkiran","submitted_at":"2019-01-02T20:54:07Z","abstract_excerpt":"Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. Towards explaining this gap, we highlight the hypothesis that $\\textit{robust classification may require more complex classifiers (i.e. more capacity) than standard classification.}$\n  In this note, we show that this hypothesis is indeed possible, by giving several theoretical examples of classification tasks and sets of \"simple\" classifiers fo"},"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":"1901.00532","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-02T20:54:07Z","cross_cats_sorted":["cs.CC","stat.ML"],"title_canon_sha256":"556b08310ef4fdb8913d88bf3bea53b9d54bd3e58c375d435583467af6efc184","abstract_canon_sha256":"1e22439403d651521d943c26c9b918e31b5c167ebd326d2dea9b8cd03c8c3fcd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:02.223197Z","signature_b64":"EB+cXe7/DDHEEaz0oD9RrQ3jl4q7lhxmY0QOD15eycTZZBFoxGKczWoUjI6WX2jpRJT7vKeOttSUA/f+7Ov5Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2938220a6263615c7c5ed15291a2d6c6e4880ea2fc6d157f0bea7cb214de66c9","last_reissued_at":"2026-05-17T23:57:02.222605Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:02.222605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Robustness May Be at Odds With Simplicity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Preetum Nakkiran","submitted_at":"2019-01-02T20:54:07Z","abstract_excerpt":"Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. Towards explaining this gap, we highlight the hypothesis that $\\textit{robust classification may require more complex classifiers (i.e. more capacity) than standard classification.}$\n  In this note, we show that this hypothesis is indeed possible, by giving several theoretical examples of classification tasks and sets of \"simple\" classifiers fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.00532","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":"1901.00532","created_at":"2026-05-17T23:57:02.222685+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.00532v1","created_at":"2026-05-17T23:57:02.222685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.00532","created_at":"2026-05-17T23:57:02.222685+00:00"},{"alias_kind":"pith_short_12","alias_value":"FE4CECTCMNQV","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"FE4CECTCMNQVY7C6","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"FE4CECTC","created_at":"2026-05-18T12:33:15.570797+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/FE4CECTCMNQVY7C62FJJDIWWY3","json":"https://pith.science/pith/FE4CECTCMNQVY7C62FJJDIWWY3.json","graph_json":"https://pith.science/api/pith-number/FE4CECTCMNQVY7C62FJJDIWWY3/graph.json","events_json":"https://pith.science/api/pith-number/FE4CECTCMNQVY7C62FJJDIWWY3/events.json","paper":"https://pith.science/paper/FE4CECTC"},"agent_actions":{"view_html":"https://pith.science/pith/FE4CECTCMNQVY7C62FJJDIWWY3","download_json":"https://pith.science/pith/FE4CECTCMNQVY7C62FJJDIWWY3.json","view_paper":"https://pith.science/paper/FE4CECTC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.00532&json=true","fetch_graph":"https://pith.science/api/pith-number/FE4CECTCMNQVY7C62FJJDIWWY3/graph.json","fetch_events":"https://pith.science/api/pith-number/FE4CECTCMNQVY7C62FJJDIWWY3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FE4CECTCMNQVY7C62FJJDIWWY3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FE4CECTCMNQVY7C62FJJDIWWY3/action/storage_attestation","attest_author":"https://pith.science/pith/FE4CECTCMNQVY7C62FJJDIWWY3/action/author_attestation","sign_citation":"https://pith.science/pith/FE4CECTCMNQVY7C62FJJDIWWY3/action/citation_signature","submit_replication":"https://pith.science/pith/FE4CECTCMNQVY7C62FJJDIWWY3/action/replication_record"}},"created_at":"2026-05-17T23:57:02.222685+00:00","updated_at":"2026-05-17T23:57:02.222685+00:00"}