{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:3MUJQZRCPG26CVEKDJCHJKVYA3","short_pith_number":"pith:3MUJQZRC","canonical_record":{"source":{"id":"1904.06314","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-12T16:44:10Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7c3ff6b68aa74400c504b8c4ac99938b624764cca8b8c09ccc715f7bd499ce62","abstract_canon_sha256":"c925d9fe4b414c522e641c1a62559b1f1c79cd68494d4322d44c9834e127a46c"},"schema_version":"1.0"},"canonical_sha256":"db2898662279b5e1548a1a4474aab806d5f28082b0270d04655b95dc07e517ff","source":{"kind":"arxiv","id":"1904.06314","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.06314","created_at":"2026-05-17T23:48:43Z"},{"alias_kind":"arxiv_version","alias_value":"1904.06314v1","created_at":"2026-05-17T23:48:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.06314","created_at":"2026-05-17T23:48:43Z"},{"alias_kind":"pith_short_12","alias_value":"3MUJQZRCPG26","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"3MUJQZRCPG26CVEK","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"3MUJQZRC","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:3MUJQZRCPG26CVEKDJCHJKVYA3","target":"record","payload":{"canonical_record":{"source":{"id":"1904.06314","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-12T16:44:10Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"7c3ff6b68aa74400c504b8c4ac99938b624764cca8b8c09ccc715f7bd499ce62","abstract_canon_sha256":"c925d9fe4b414c522e641c1a62559b1f1c79cd68494d4322d44c9834e127a46c"},"schema_version":"1.0"},"canonical_sha256":"db2898662279b5e1548a1a4474aab806d5f28082b0270d04655b95dc07e517ff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:43.616520Z","signature_b64":"pHm6f7ikeZ0BuWL2VJ1xe0eI++goVaOFKDX7+U2VZt9GaSJv83/SstzZKF5wx2MLR6BinTtTLZcMNqm49ZVyBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db2898662279b5e1548a1a4474aab806d5f28082b0270d04655b95dc07e517ff","last_reissued_at":"2026-05-17T23:48:43.615982Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:43.615982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.06314","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kkalRCeX8QmX5MemSIuW0cXAQmTOklQ5vhQKjJG/8HK4syn5dhHsA8x83pCysjdxIVDBMI7Lr3hMV28TO4UBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T21:45:18.399418Z"},"content_sha256":"58cf998be89f84f2950c9cecfdb823a834daf15d3bc34becf85beff709fd1ebf","schema_version":"1.0","event_id":"sha256:58cf998be89f84f2950c9cecfdb823a834daf15d3bc34becf85beff709fd1ebf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:3MUJQZRCPG26CVEKDJCHJKVYA3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Optimal Decision Trees from Large Datasets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Florent Avellaneda","submitted_at":"2019-04-12T16:44:10Z","abstract_excerpt":"Inferring a decision tree from a given dataset is one of the classic problems in machine learning. This problem consists of buildings, from a labelled dataset, a tree such that each node corresponds to a class and a path between the tree root and a leaf corresponds to a conjunction of features to be satisfied in this class. Following the principle of parsimony, we want to infer a minimal tree consistent with the dataset. Unfortunately, inferring an optimal decision tree is known to be NP-complete for several definitions of optimality. Hence, the majority of existing approaches relies on heuris"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.06314","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:48:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8ySrv4m2tDd7AwtXuWn0OK9VXUP0hWRnfiwRE7qYWWmTKnil055U7oRhnmomiFgzJJ4uCG5bX+iOQjuS+qNMDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-29T21:45:18.400065Z"},"content_sha256":"51a9e2f41be5b3e364412a976eadcf22df11997fb398f364b371967570dda2df","schema_version":"1.0","event_id":"sha256:51a9e2f41be5b3e364412a976eadcf22df11997fb398f364b371967570dda2df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3MUJQZRCPG26CVEKDJCHJKVYA3/bundle.json","state_url":"https://pith.science/pith/3MUJQZRCPG26CVEKDJCHJKVYA3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3MUJQZRCPG26CVEKDJCHJKVYA3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-29T21:45:18Z","links":{"resolver":"https://pith.science/pith/3MUJQZRCPG26CVEKDJCHJKVYA3","bundle":"https://pith.science/pith/3MUJQZRCPG26CVEKDJCHJKVYA3/bundle.json","state":"https://pith.science/pith/3MUJQZRCPG26CVEKDJCHJKVYA3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3MUJQZRCPG26CVEKDJCHJKVYA3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3MUJQZRCPG26CVEKDJCHJKVYA3","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":"c925d9fe4b414c522e641c1a62559b1f1c79cd68494d4322d44c9834e127a46c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-12T16:44:10Z","title_canon_sha256":"7c3ff6b68aa74400c504b8c4ac99938b624764cca8b8c09ccc715f7bd499ce62"},"schema_version":"1.0","source":{"id":"1904.06314","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.06314","created_at":"2026-05-17T23:48:43Z"},{"alias_kind":"arxiv_version","alias_value":"1904.06314v1","created_at":"2026-05-17T23:48:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.06314","created_at":"2026-05-17T23:48:43Z"},{"alias_kind":"pith_short_12","alias_value":"3MUJQZRCPG26","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"3MUJQZRCPG26CVEK","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"3MUJQZRC","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:51a9e2f41be5b3e364412a976eadcf22df11997fb398f364b371967570dda2df","target":"graph","created_at":"2026-05-17T23:48:43Z","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":"Inferring a decision tree from a given dataset is one of the classic problems in machine learning. This problem consists of buildings, from a labelled dataset, a tree such that each node corresponds to a class and a path between the tree root and a leaf corresponds to a conjunction of features to be satisfied in this class. Following the principle of parsimony, we want to infer a minimal tree consistent with the dataset. Unfortunately, inferring an optimal decision tree is known to be NP-complete for several definitions of optimality. Hence, the majority of existing approaches relies on heuris","authors_text":"Florent Avellaneda","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-12T16:44:10Z","title":"Learning Optimal Decision Trees from Large Datasets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.06314","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":"sha256:58cf998be89f84f2950c9cecfdb823a834daf15d3bc34becf85beff709fd1ebf","target":"record","created_at":"2026-05-17T23:48:43Z","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":"c925d9fe4b414c522e641c1a62559b1f1c79cd68494d4322d44c9834e127a46c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-12T16:44:10Z","title_canon_sha256":"7c3ff6b68aa74400c504b8c4ac99938b624764cca8b8c09ccc715f7bd499ce62"},"schema_version":"1.0","source":{"id":"1904.06314","kind":"arxiv","version":1}},"canonical_sha256":"db2898662279b5e1548a1a4474aab806d5f28082b0270d04655b95dc07e517ff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db2898662279b5e1548a1a4474aab806d5f28082b0270d04655b95dc07e517ff","first_computed_at":"2026-05-17T23:48:43.615982Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:43.615982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pHm6f7ikeZ0BuWL2VJ1xe0eI++goVaOFKDX7+U2VZt9GaSJv83/SstzZKF5wx2MLR6BinTtTLZcMNqm49ZVyBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:43.616520Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.06314","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:58cf998be89f84f2950c9cecfdb823a834daf15d3bc34becf85beff709fd1ebf","sha256:51a9e2f41be5b3e364412a976eadcf22df11997fb398f364b371967570dda2df"],"state_sha256":"f48ad6779d8a3b7baff60065ebae77bb0dbbb081c9f4d2bd28bd6b651bcc9436"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QmyRf7Irf7KYYMhe7BodvfwBvwvt+enwXboFDUaCaF1anIPr/Ty5zo7r5YJlOEztokdRWl1cBJRCT2LSFdo0Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-29T21:45:18.403167Z","bundle_sha256":"1234b4d6535cffa66d9e4b7fd1025d5133bda1efaea1a0ef32b9f4e2b59b2615"}}