{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:AEK4ZFBMFSUT6XLCYTP53KCQHX","short_pith_number":"pith:AEK4ZFBM","schema_version":"1.0","canonical_sha256":"0115cc942c2ca93f5d62c4dfdda8503dfb3465377ad7c083aef9d0b687592357","source":{"kind":"arxiv","id":"1704.01701","version":4},"attestation_state":"computed","paper":{"title":"Learning Certifiably Optimal Rule Lists for Categorical Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Cynthia Rudin, Daniel Alabi, Elaine Angelino, Margo Seltzer, Nicholas Larus-Stone","submitted_at":"2017-04-06T04:02:35Z","abstract_excerpt":"We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm produces rule lists with optimal training performance, according to the regularized empirical risk, with a certificate of optimality. By leveraging algorithmic bounds, efficient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. Our results indicate that "},"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":"1704.01701","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-04-06T04:02:35Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"dbf781f55b284becbb0ade5595267e34a7c9e0d6712452346983c39e494229f2","abstract_canon_sha256":"36db0fa00e7880ed0ee417ede0622061b9b324b6f180beffb304efb9d531fa47"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:55.416174Z","signature_b64":"J6BelDIdGWTGMOn3YXLYhpm0G/E+aOSLUXsu7ApZSkddg4pcxeKfpK0nLkxQXJ9dKOP5PGB4V2VahX9bHrr4Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0115cc942c2ca93f5d62c4dfdda8503dfb3465377ad7c083aef9d0b687592357","last_reissued_at":"2026-05-18T00:08:55.415661Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:55.415661Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Certifiably Optimal Rule Lists for Categorical Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Cynthia Rudin, Daniel Alabi, Elaine Angelino, Margo Seltzer, Nicholas Larus-Stone","submitted_at":"2017-04-06T04:02:35Z","abstract_excerpt":"We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm produces rule lists with optimal training performance, according to the regularized empirical risk, with a certificate of optimality. By leveraging algorithmic bounds, efficient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. Our results indicate that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.01701","kind":"arxiv","version":4},"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":"1704.01701","created_at":"2026-05-18T00:08:55.415737+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.01701v4","created_at":"2026-05-18T00:08:55.415737+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.01701","created_at":"2026-05-18T00:08:55.415737+00:00"},{"alias_kind":"pith_short_12","alias_value":"AEK4ZFBMFSUT","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_16","alias_value":"AEK4ZFBMFSUT6XLC","created_at":"2026-05-18T12:31:05.417338+00:00"},{"alias_kind":"pith_short_8","alias_value":"AEK4ZFBM","created_at":"2026-05-18T12:31:05.417338+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/AEK4ZFBMFSUT6XLCYTP53KCQHX","json":"https://pith.science/pith/AEK4ZFBMFSUT6XLCYTP53KCQHX.json","graph_json":"https://pith.science/api/pith-number/AEK4ZFBMFSUT6XLCYTP53KCQHX/graph.json","events_json":"https://pith.science/api/pith-number/AEK4ZFBMFSUT6XLCYTP53KCQHX/events.json","paper":"https://pith.science/paper/AEK4ZFBM"},"agent_actions":{"view_html":"https://pith.science/pith/AEK4ZFBMFSUT6XLCYTP53KCQHX","download_json":"https://pith.science/pith/AEK4ZFBMFSUT6XLCYTP53KCQHX.json","view_paper":"https://pith.science/paper/AEK4ZFBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.01701&json=true","fetch_graph":"https://pith.science/api/pith-number/AEK4ZFBMFSUT6XLCYTP53KCQHX/graph.json","fetch_events":"https://pith.science/api/pith-number/AEK4ZFBMFSUT6XLCYTP53KCQHX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AEK4ZFBMFSUT6XLCYTP53KCQHX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AEK4ZFBMFSUT6XLCYTP53KCQHX/action/storage_attestation","attest_author":"https://pith.science/pith/AEK4ZFBMFSUT6XLCYTP53KCQHX/action/author_attestation","sign_citation":"https://pith.science/pith/AEK4ZFBMFSUT6XLCYTP53KCQHX/action/citation_signature","submit_replication":"https://pith.science/pith/AEK4ZFBMFSUT6XLCYTP53KCQHX/action/replication_record"}},"created_at":"2026-05-18T00:08:55.415737+00:00","updated_at":"2026-05-18T00:08:55.415737+00:00"}