{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VLJLCP6GPFH52YURXRVYQ35A3W","short_pith_number":"pith:VLJLCP6G","canonical_record":{"source":{"id":"2410.03535","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-04T15:54:02Z","cross_cats_sorted":[],"title_canon_sha256":"e42b07c2b8b3993c98e02508ddc660ac005c0a3e90d49a59e44002024018e2e1","abstract_canon_sha256":"c5cb4548142c0436f1e393eb9448489ad8a6a5ca4f81b128bd629827c2d9b95f"},"schema_version":"1.0"},"canonical_sha256":"aad2b13fc6794fdd6291bc6b886fa0dd8edc269381ff771f7ade0e7f8cf431e5","source":{"kind":"arxiv","id":"2410.03535","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.03535","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"arxiv_version","alias_value":"2410.03535v2","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03535","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"pith_short_12","alias_value":"VLJLCP6GPFH5","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"pith_short_16","alias_value":"VLJLCP6GPFH52YUR","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"pith_short_8","alias_value":"VLJLCP6G","created_at":"2026-07-05T10:50:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VLJLCP6GPFH52YURXRVYQ35A3W","target":"record","payload":{"canonical_record":{"source":{"id":"2410.03535","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-04T15:54:02Z","cross_cats_sorted":[],"title_canon_sha256":"e42b07c2b8b3993c98e02508ddc660ac005c0a3e90d49a59e44002024018e2e1","abstract_canon_sha256":"c5cb4548142c0436f1e393eb9448489ad8a6a5ca4f81b128bd629827c2d9b95f"},"schema_version":"1.0"},"canonical_sha256":"aad2b13fc6794fdd6291bc6b886fa0dd8edc269381ff771f7ade0e7f8cf431e5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:44.367909Z","signature_b64":"OGJjUTSsyWJL4QAiQj36wyQMFZ3j0VrZKgr38bpuRYX91AZvV2COE7QKEODxKeq+ua3Ep19PO83vZMVMy2E4AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aad2b13fc6794fdd6291bc6b886fa0dd8edc269381ff771f7ade0e7f8cf431e5","last_reissued_at":"2026-07-05T10:50:44.367407Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:44.367407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.03535","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g9hI1fnIda7EiZ+jJ0NvFQ7ZEZ7pZeraWHD2qxF8Pri0Pou1oo6GAIR3T5+eboNucrm5lR3mYDmCIR90Pyq3Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T03:54:49.034927Z"},"content_sha256":"55ae079785aa298713abbf61d495dc2bb22ad1128f47580fdbe24dda267aceed","schema_version":"1.0","event_id":"sha256:55ae079785aa298713abbf61d495dc2bb22ad1128f47580fdbe24dda267aceed"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VLJLCP6GPFH52YURXRVYQ35A3W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NRGBoost: Energy-Based Generative Boosted Trees","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jo\\~ao Bravo","submitted_at":"2024-10-04T15:54:02Z","abstract_excerpt":"Despite the rise to dominance of deep learning in unstructured data domains, tree-based methods such as Random Forests (RF) and Gradient Boosted Decision Trees (GBDT) are still the workhorses for handling discriminative tasks on tabular data. We explore generative extensions of these popular algorithms with a focus on explicitly modeling the data density (up to a normalization constant), thus enabling other applications besides sampling. As our main contribution we propose an energy-based generative boosting algorithm that is analogous to the second-order boosting implemented in popular librar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03535","kind":"arxiv","version":2},"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/2410.03535/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kIuBHD7Bc6ULaZXlgnUwYARKjUzloIQ9L0pDJVekSLLuZZ3cwZak0KFa+H07ew5CZWnb/4XUJglACuRMJAFYAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T03:54:49.035806Z"},"content_sha256":"42daad828cfa5f998a0f93697b8044b15d5bebe4733220820c9bda21ee7bbcdb","schema_version":"1.0","event_id":"sha256:42daad828cfa5f998a0f93697b8044b15d5bebe4733220820c9bda21ee7bbcdb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VLJLCP6GPFH52YURXRVYQ35A3W/bundle.json","state_url":"https://pith.science/pith/VLJLCP6GPFH52YURXRVYQ35A3W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VLJLCP6GPFH52YURXRVYQ35A3W/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":"2026-08-22T03:54:49Z","links":{"resolver":"https://pith.science/pith/VLJLCP6GPFH52YURXRVYQ35A3W","bundle":"https://pith.science/pith/VLJLCP6GPFH52YURXRVYQ35A3W/bundle.json","state":"https://pith.science/pith/VLJLCP6GPFH52YURXRVYQ35A3W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VLJLCP6GPFH52YURXRVYQ35A3W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VLJLCP6GPFH52YURXRVYQ35A3W","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":"c5cb4548142c0436f1e393eb9448489ad8a6a5ca4f81b128bd629827c2d9b95f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-04T15:54:02Z","title_canon_sha256":"e42b07c2b8b3993c98e02508ddc660ac005c0a3e90d49a59e44002024018e2e1"},"schema_version":"1.0","source":{"id":"2410.03535","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.03535","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"arxiv_version","alias_value":"2410.03535v2","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03535","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"pith_short_12","alias_value":"VLJLCP6GPFH5","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"pith_short_16","alias_value":"VLJLCP6GPFH52YUR","created_at":"2026-07-05T10:50:44Z"},{"alias_kind":"pith_short_8","alias_value":"VLJLCP6G","created_at":"2026-07-05T10:50:44Z"}],"graph_snapshots":[{"event_id":"sha256:42daad828cfa5f998a0f93697b8044b15d5bebe4733220820c9bda21ee7bbcdb","target":"graph","created_at":"2026-07-05T10:50:44Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2410.03535/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite the rise to dominance of deep learning in unstructured data domains, tree-based methods such as Random Forests (RF) and Gradient Boosted Decision Trees (GBDT) are still the workhorses for handling discriminative tasks on tabular data. We explore generative extensions of these popular algorithms with a focus on explicitly modeling the data density (up to a normalization constant), thus enabling other applications besides sampling. As our main contribution we propose an energy-based generative boosting algorithm that is analogous to the second-order boosting implemented in popular librar","authors_text":"Jo\\~ao Bravo","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-04T15:54:02Z","title":"NRGBoost: Energy-Based Generative Boosted Trees"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03535","kind":"arxiv","version":2},"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":"sha256:55ae079785aa298713abbf61d495dc2bb22ad1128f47580fdbe24dda267aceed","target":"record","created_at":"2026-07-05T10:50:44Z","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":"c5cb4548142c0436f1e393eb9448489ad8a6a5ca4f81b128bd629827c2d9b95f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-04T15:54:02Z","title_canon_sha256":"e42b07c2b8b3993c98e02508ddc660ac005c0a3e90d49a59e44002024018e2e1"},"schema_version":"1.0","source":{"id":"2410.03535","kind":"arxiv","version":2}},"canonical_sha256":"aad2b13fc6794fdd6291bc6b886fa0dd8edc269381ff771f7ade0e7f8cf431e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aad2b13fc6794fdd6291bc6b886fa0dd8edc269381ff771f7ade0e7f8cf431e5","first_computed_at":"2026-07-05T10:50:44.367407Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:44.367407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OGJjUTSsyWJL4QAiQj36wyQMFZ3j0VrZKgr38bpuRYX91AZvV2COE7QKEODxKeq+ua3Ep19PO83vZMVMy2E4AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:44.367909Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.03535","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:55ae079785aa298713abbf61d495dc2bb22ad1128f47580fdbe24dda267aceed","sha256:42daad828cfa5f998a0f93697b8044b15d5bebe4733220820c9bda21ee7bbcdb"],"state_sha256":"96f3be4bba5d215f6c4770834c10e4c3901c78655369b3f3543ef68741c54854"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pyUxr3SRRJy9tDO2465KekkMqNQMvA0zSTSI+TrUGyy6neSEMKP7U0Vxq1Z8yMDsQZnFbcIaJgGsdmwm/IfHCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T03:54:49.059193Z","bundle_sha256":"f9d5fa9983d1945bce3413dd68f5040a6c9f46d6d29d91fe076f49b2a533be42"}}