{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NEUTBJDF25WAFNYWHVTH4A6FML","short_pith_number":"pith:NEUTBJDF","schema_version":"1.0","canonical_sha256":"692930a465d76c02b7163d667e03c562f8f26d1d0d954798c0e7335b45f10225","source":{"kind":"arxiv","id":"2310.04916","version":1},"attestation_state":"computed","paper":{"title":"Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Brendon G. Anderson, Samuel Pfrommer, Somayeh Sojoudi","submitted_at":"2023-10-07T21:07:45Z","abstract_excerpt":"The reliable deployment of neural networks in control systems requires rigorous robustness guarantees. In this paper, we obtain tight robustness certificates over convex attack sets for min-max representations of ReLU neural networks by developing a convex reformulation of the nonconvex certification problem. This is done by \"lifting\" the problem to an infinite-dimensional optimization over probability measures, leveraging recent results in distributionally robust optimization to solve for an optimal discrete distribution, and proving that solutions of the original nonconvex problem are genera"},"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":"2310.04916","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2023-10-07T21:07:45Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"82b8f32d77d1426c120dee924f9b9be46faae4ca40806e53371bb80fe464700a","abstract_canon_sha256":"68cfe29bf74e9dfdf9afad3abdf9d6584b2df96a6d36679ef90a620d8b36390a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:58:24.564565Z","signature_b64":"8j41SQELKLTJclcjZqzjIhmiFJyZForKSjtHmATh1JGS1fKMRfen7A1hP5RdjGFCl4iUKLB7bhuy8TrXEUV2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"692930a465d76c02b7163d667e03c562f8f26d1d0d954798c0e7335b45f10225","last_reissued_at":"2026-07-05T06:58:24.564200Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:58:24.564200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Brendon G. Anderson, Samuel Pfrommer, Somayeh Sojoudi","submitted_at":"2023-10-07T21:07:45Z","abstract_excerpt":"The reliable deployment of neural networks in control systems requires rigorous robustness guarantees. In this paper, we obtain tight robustness certificates over convex attack sets for min-max representations of ReLU neural networks by developing a convex reformulation of the nonconvex certification problem. This is done by \"lifting\" the problem to an infinite-dimensional optimization over probability measures, leveraging recent results in distributionally robust optimization to solve for an optimal discrete distribution, and proving that solutions of the original nonconvex problem are genera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04916","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.04916/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":"2310.04916","created_at":"2026-07-05T06:58:24.564256+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.04916v1","created_at":"2026-07-05T06:58:24.564256+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04916","created_at":"2026-07-05T06:58:24.564256+00:00"},{"alias_kind":"pith_short_12","alias_value":"NEUTBJDF25WA","created_at":"2026-07-05T06:58:24.564256+00:00"},{"alias_kind":"pith_short_16","alias_value":"NEUTBJDF25WAFNYW","created_at":"2026-07-05T06:58:24.564256+00:00"},{"alias_kind":"pith_short_8","alias_value":"NEUTBJDF","created_at":"2026-07-05T06:58:24.564256+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/NEUTBJDF25WAFNYWHVTH4A6FML","json":"https://pith.science/pith/NEUTBJDF25WAFNYWHVTH4A6FML.json","graph_json":"https://pith.science/api/pith-number/NEUTBJDF25WAFNYWHVTH4A6FML/graph.json","events_json":"https://pith.science/api/pith-number/NEUTBJDF25WAFNYWHVTH4A6FML/events.json","paper":"https://pith.science/paper/NEUTBJDF"},"agent_actions":{"view_html":"https://pith.science/pith/NEUTBJDF25WAFNYWHVTH4A6FML","download_json":"https://pith.science/pith/NEUTBJDF25WAFNYWHVTH4A6FML.json","view_paper":"https://pith.science/paper/NEUTBJDF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.04916&json=true","fetch_graph":"https://pith.science/api/pith-number/NEUTBJDF25WAFNYWHVTH4A6FML/graph.json","fetch_events":"https://pith.science/api/pith-number/NEUTBJDF25WAFNYWHVTH4A6FML/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NEUTBJDF25WAFNYWHVTH4A6FML/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NEUTBJDF25WAFNYWHVTH4A6FML/action/storage_attestation","attest_author":"https://pith.science/pith/NEUTBJDF25WAFNYWHVTH4A6FML/action/author_attestation","sign_citation":"https://pith.science/pith/NEUTBJDF25WAFNYWHVTH4A6FML/action/citation_signature","submit_replication":"https://pith.science/pith/NEUTBJDF25WAFNYWHVTH4A6FML/action/replication_record"}},"created_at":"2026-07-05T06:58:24.564256+00:00","updated_at":"2026-07-05T06:58:24.564256+00:00"}