{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:FPAB7MBVIVURF4S6ANX7DJ4LUN","short_pith_number":"pith:FPAB7MBV","canonical_record":{"source":{"id":"1808.02123","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-06T21:27:05Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"d98698250df73c0c803f2509801c7a165ee58d553e5b2f4b224b77d5a9ba27e1","abstract_canon_sha256":"fdb474891e52adaf5bec577bc0af6d4d382225dcb27631922a429c82b5ca904e"},"schema_version":"1.0"},"canonical_sha256":"2bc01fb035456912f25e036ff1a78ba37c39ea09f934af9573ed8806c4c9452d","source":{"kind":"arxiv","id":"1808.02123","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.02123","created_at":"2026-05-18T00:08:47Z"},{"alias_kind":"arxiv_version","alias_value":"1808.02123v1","created_at":"2026-05-18T00:08:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.02123","created_at":"2026-05-18T00:08:47Z"},{"alias_kind":"pith_short_12","alias_value":"FPAB7MBVIVUR","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FPAB7MBVIVURF4S6","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FPAB7MBV","created_at":"2026-05-18T12:32:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:FPAB7MBVIVURF4S6ANX7DJ4LUN","target":"record","payload":{"canonical_record":{"source":{"id":"1808.02123","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-06T21:27:05Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"d98698250df73c0c803f2509801c7a165ee58d553e5b2f4b224b77d5a9ba27e1","abstract_canon_sha256":"fdb474891e52adaf5bec577bc0af6d4d382225dcb27631922a429c82b5ca904e"},"schema_version":"1.0"},"canonical_sha256":"2bc01fb035456912f25e036ff1a78ba37c39ea09f934af9573ed8806c4c9452d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:47.552007Z","signature_b64":"9UzvaCuUWPAttL9cuFAULulzp6xgNXWIQ6KlUaCMlBaQQ6+867M30Cx0DDskVvkm9+RLEtDWBFqnIC3kDPGaBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bc01fb035456912f25e036ff1a78ba37c39ea09f934af9573ed8806c4c9452d","last_reissued_at":"2026-05-18T00:08:47.551430Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:47.551430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.02123","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":"2026-05-18T00:08:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rmSkUbPcIUgS/EdhBHWTZo2g40XV53DtMyemaVfskKrmtcV7vlRJUQkUCc7/ZJVKpkSvkjpPR564e0AoVOUPAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T21:06:46.029492Z"},"content_sha256":"7b282f63b08fd41231e7b3fe075575ebd0bc72b4920ae6b926d74d841ae82e30","schema_version":"1.0","event_id":"sha256:7b282f63b08fd41231e7b3fe075575ebd0bc72b4920ae6b926d74d841ae82e30"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:FPAB7MBVIVURF4S6ANX7DJ4LUN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Structure Learning for Relational Logistic Regression: An Ensemble Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bahare Fatemi, David Poole, Gautam Kunapuli, Kristian Kersting, Nandini Ramanan, Seyed Mehran Kazemi, Sriraam Natarajan, Tushar Khot","submitted_at":"2018-08-06T21:27:05Z","abstract_excerpt":"We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on the recently successful functional-gradient boosting methods for probabilistic logic models. We derive the functional gradients and show how weights can be learned simultaneously in an efficient manner. Our empirical evaluation on standard and novel data sets demo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.02123","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":"2026-05-18T00:08:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jdhsAFTe3mndV8uHcJJHYxUyy6fuvUCPWiophvbcNhMqQIBd6xQzl0FDpPS/S2L3LBorFfxpbztkKBealo9sCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T21:06:46.029865Z"},"content_sha256":"8e7feeb489539ba66cbe2cece1bd9ae325f86ef8053ca5c01b052171ff25a557","schema_version":"1.0","event_id":"sha256:8e7feeb489539ba66cbe2cece1bd9ae325f86ef8053ca5c01b052171ff25a557"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FPAB7MBVIVURF4S6ANX7DJ4LUN/bundle.json","state_url":"https://pith.science/pith/FPAB7MBVIVURF4S6ANX7DJ4LUN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FPAB7MBVIVURF4S6ANX7DJ4LUN/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":"2026-05-27T21:06:46Z","links":{"resolver":"https://pith.science/pith/FPAB7MBVIVURF4S6ANX7DJ4LUN","bundle":"https://pith.science/pith/FPAB7MBVIVURF4S6ANX7DJ4LUN/bundle.json","state":"https://pith.science/pith/FPAB7MBVIVURF4S6ANX7DJ4LUN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FPAB7MBVIVURF4S6ANX7DJ4LUN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:FPAB7MBVIVURF4S6ANX7DJ4LUN","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":"fdb474891e52adaf5bec577bc0af6d4d382225dcb27631922a429c82b5ca904e","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-06T21:27:05Z","title_canon_sha256":"d98698250df73c0c803f2509801c7a165ee58d553e5b2f4b224b77d5a9ba27e1"},"schema_version":"1.0","source":{"id":"1808.02123","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.02123","created_at":"2026-05-18T00:08:47Z"},{"alias_kind":"arxiv_version","alias_value":"1808.02123v1","created_at":"2026-05-18T00:08:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.02123","created_at":"2026-05-18T00:08:47Z"},{"alias_kind":"pith_short_12","alias_value":"FPAB7MBVIVUR","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_16","alias_value":"FPAB7MBVIVURF4S6","created_at":"2026-05-18T12:32:25Z"},{"alias_kind":"pith_short_8","alias_value":"FPAB7MBV","created_at":"2026-05-18T12:32:25Z"}],"graph_snapshots":[{"event_id":"sha256:8e7feeb489539ba66cbe2cece1bd9ae325f86ef8053ca5c01b052171ff25a557","target":"graph","created_at":"2026-05-18T00:08:47Z","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":"We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on the recently successful functional-gradient boosting methods for probabilistic logic models. We derive the functional gradients and show how weights can be learned simultaneously in an efficient manner. Our empirical evaluation on standard and novel data sets demo","authors_text":"Bahare Fatemi, David Poole, Gautam Kunapuli, Kristian Kersting, Nandini Ramanan, Seyed Mehran Kazemi, Sriraam Natarajan, Tushar Khot","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-06T21:27:05Z","title":"Structure Learning for Relational Logistic Regression: An Ensemble Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.02123","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":"sha256:7b282f63b08fd41231e7b3fe075575ebd0bc72b4920ae6b926d74d841ae82e30","target":"record","created_at":"2026-05-18T00:08:47Z","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":"fdb474891e52adaf5bec577bc0af6d4d382225dcb27631922a429c82b5ca904e","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-06T21:27:05Z","title_canon_sha256":"d98698250df73c0c803f2509801c7a165ee58d553e5b2f4b224b77d5a9ba27e1"},"schema_version":"1.0","source":{"id":"1808.02123","kind":"arxiv","version":1}},"canonical_sha256":"2bc01fb035456912f25e036ff1a78ba37c39ea09f934af9573ed8806c4c9452d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2bc01fb035456912f25e036ff1a78ba37c39ea09f934af9573ed8806c4c9452d","first_computed_at":"2026-05-18T00:08:47.551430Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:08:47.551430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9UzvaCuUWPAttL9cuFAULulzp6xgNXWIQ6KlUaCMlBaQQ6+867M30Cx0DDskVvkm9+RLEtDWBFqnIC3kDPGaBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:08:47.552007Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.02123","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b282f63b08fd41231e7b3fe075575ebd0bc72b4920ae6b926d74d841ae82e30","sha256:8e7feeb489539ba66cbe2cece1bd9ae325f86ef8053ca5c01b052171ff25a557"],"state_sha256":"6a122f76411c538c13acbed0ac7db7d8e1ea14b05dc4b2cea0a3fabf5f96fbc8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nPQ28tfePuLHSIHIbSUQdLBwpZvwLnBa8RDbiL0kROBrYHqhMrkDBqVKu5MG8WLyHBORnQE46ml3zz98bo/bBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T21:06:46.033066Z","bundle_sha256":"cd2a5846fde6e3261105b062d7d0780fb65a6f46a9fc2ff6903f0944a77840d3"}}