{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EFS4Q6XEN6ACOKJXY6RAQPS54S","short_pith_number":"pith:EFS4Q6XE","schema_version":"1.0","canonical_sha256":"2165c87ae46f80272937c7a2083e5de49881479b4ca86eb77743df4b9032cc62","source":{"kind":"arxiv","id":"2506.04360","version":1},"attestation_state":"computed","paper":{"title":"Even Faster Hyperbolic Random Forests: A Beltrami-Klein Wrapper Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Itsik Pe'er, Philippe Chlenski","submitted_at":"2025-06-04T18:20:30Z","abstract_excerpt":"Decision trees and models that use them as primitives are workhorses of machine learning in Euclidean spaces. Recent work has further extended these models to the Lorentz model of hyperbolic space by replacing axis-parallel hyperplanes with homogeneous hyperplanes when partitioning the input space. In this paper, we show how the hyperDT algorithm can be elegantly reexpressed in the Beltrami-Klein model of hyperbolic spaces. This preserves the thresholding operation used in Euclidean decision trees, enabling us to further rewrite hyperDT as simple pre- and post-processing steps that form a wrap"},"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":"2506.04360","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-04T18:20:30Z","cross_cats_sorted":[],"title_canon_sha256":"c8c819f725b28e5e0893dbffe97b60c02750d51d53addca9546359d231e081c9","abstract_canon_sha256":"fb8daa17e874555a6b301a84c06196e33ff33b095dc376b7c490bebce4d2acfe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:13.215803Z","signature_b64":"8iN3ZBcmdAfJtuPsTPOqPlSaLVfEv99qpphiY7qRAWqTXtx6vUcaL4PL2PykweP2+8VtL51zsBCCJsW3biVjBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2165c87ae46f80272937c7a2083e5de49881479b4ca86eb77743df4b9032cc62","last_reissued_at":"2026-07-05T11:16:13.215292Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:13.215292Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Even Faster Hyperbolic Random Forests: A Beltrami-Klein Wrapper Approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Itsik Pe'er, Philippe Chlenski","submitted_at":"2025-06-04T18:20:30Z","abstract_excerpt":"Decision trees and models that use them as primitives are workhorses of machine learning in Euclidean spaces. Recent work has further extended these models to the Lorentz model of hyperbolic space by replacing axis-parallel hyperplanes with homogeneous hyperplanes when partitioning the input space. In this paper, we show how the hyperDT algorithm can be elegantly reexpressed in the Beltrami-Klein model of hyperbolic spaces. This preserves the thresholding operation used in Euclidean decision trees, enabling us to further rewrite hyperDT as simple pre- and post-processing steps that form a wrap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04360","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/2506.04360/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":"2506.04360","created_at":"2026-07-05T11:16:13.215378+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.04360v1","created_at":"2026-07-05T11:16:13.215378+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04360","created_at":"2026-07-05T11:16:13.215378+00:00"},{"alias_kind":"pith_short_12","alias_value":"EFS4Q6XEN6AC","created_at":"2026-07-05T11:16:13.215378+00:00"},{"alias_kind":"pith_short_16","alias_value":"EFS4Q6XEN6ACOKJX","created_at":"2026-07-05T11:16:13.215378+00:00"},{"alias_kind":"pith_short_8","alias_value":"EFS4Q6XE","created_at":"2026-07-05T11:16:13.215378+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/EFS4Q6XEN6ACOKJXY6RAQPS54S","json":"https://pith.science/pith/EFS4Q6XEN6ACOKJXY6RAQPS54S.json","graph_json":"https://pith.science/api/pith-number/EFS4Q6XEN6ACOKJXY6RAQPS54S/graph.json","events_json":"https://pith.science/api/pith-number/EFS4Q6XEN6ACOKJXY6RAQPS54S/events.json","paper":"https://pith.science/paper/EFS4Q6XE"},"agent_actions":{"view_html":"https://pith.science/pith/EFS4Q6XEN6ACOKJXY6RAQPS54S","download_json":"https://pith.science/pith/EFS4Q6XEN6ACOKJXY6RAQPS54S.json","view_paper":"https://pith.science/paper/EFS4Q6XE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.04360&json=true","fetch_graph":"https://pith.science/api/pith-number/EFS4Q6XEN6ACOKJXY6RAQPS54S/graph.json","fetch_events":"https://pith.science/api/pith-number/EFS4Q6XEN6ACOKJXY6RAQPS54S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EFS4Q6XEN6ACOKJXY6RAQPS54S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EFS4Q6XEN6ACOKJXY6RAQPS54S/action/storage_attestation","attest_author":"https://pith.science/pith/EFS4Q6XEN6ACOKJXY6RAQPS54S/action/author_attestation","sign_citation":"https://pith.science/pith/EFS4Q6XEN6ACOKJXY6RAQPS54S/action/citation_signature","submit_replication":"https://pith.science/pith/EFS4Q6XEN6ACOKJXY6RAQPS54S/action/replication_record"}},"created_at":"2026-07-05T11:16:13.215378+00:00","updated_at":"2026-07-05T11:16:13.215378+00:00"}