{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YPSCVTMT3SJLBLCI5NOJHXIS6J","short_pith_number":"pith:YPSCVTMT","schema_version":"1.0","canonical_sha256":"c3e42acd93dc92b0ac48eb5c93dd12f25435e95a2f493da3193e514795c5654e","source":{"kind":"arxiv","id":"2402.11228","version":2},"attestation_state":"computed","paper":{"title":"Adaptive Split Balancing for Optimal Random Forest","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ME","stat.TH"],"primary_cat":"stat.ML","authors_text":"Jelena Bradic, Weijie Ji, Yuqian Zhang","submitted_at":"2024-02-17T09:10:40Z","abstract_excerpt":"In this paper, we propose a new random forest algorithm that constructs the trees using a novel adaptive split-balancing method. Rather than relying on the widely-used random feature selection, we propose a permutation-based balanced splitting criterion. The adaptive split balancing forest (ASBF), achieves minimax optimality under the Lipschitz class. Its localized version, which fits local regressions at the leaf level, attains the minimax rate under the broad H\\\"older class $\\mathcal{H}^{q,\\beta}$ of problems for any $q\\in\\mathbb{N}$ and $\\beta\\in(0,1]$. We identify that over-reliance on aux"},"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":"2402.11228","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-02-17T09:10:40Z","cross_cats_sorted":["cs.LG","math.ST","stat.ME","stat.TH"],"title_canon_sha256":"ca9ba2a9a05d76c59fd580b2c640817140435992cc1b20af8755500b5402e775","abstract_canon_sha256":"1ae825af64e4fb4c3d418a3b4c48d4112003510c77830d66b03fac68e456a2e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:01:30.190013Z","signature_b64":"UFg25IRODbLM8z7AR7GtdSDdI6Jzmsse5gBS7jZG4fo096Sx01I7ZmdcvzEBCJzIBkwYQtxJ7EKG8bc5xyLBDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3e42acd93dc92b0ac48eb5c93dd12f25435e95a2f493da3193e514795c5654e","last_reissued_at":"2026-07-05T09:01:30.189545Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:01:30.189545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Split Balancing for Optimal Random Forest","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ME","stat.TH"],"primary_cat":"stat.ML","authors_text":"Jelena Bradic, Weijie Ji, Yuqian Zhang","submitted_at":"2024-02-17T09:10:40Z","abstract_excerpt":"In this paper, we propose a new random forest algorithm that constructs the trees using a novel adaptive split-balancing method. Rather than relying on the widely-used random feature selection, we propose a permutation-based balanced splitting criterion. The adaptive split balancing forest (ASBF), achieves minimax optimality under the Lipschitz class. Its localized version, which fits local regressions at the leaf level, attains the minimax rate under the broad H\\\"older class $\\mathcal{H}^{q,\\beta}$ of problems for any $q\\in\\mathbb{N}$ and $\\beta\\in(0,1]$. We identify that over-reliance on aux"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11228","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/2402.11228/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":"2402.11228","created_at":"2026-07-05T09:01:30.189607+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.11228v2","created_at":"2026-07-05T09:01:30.189607+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11228","created_at":"2026-07-05T09:01:30.189607+00:00"},{"alias_kind":"pith_short_12","alias_value":"YPSCVTMT3SJL","created_at":"2026-07-05T09:01:30.189607+00:00"},{"alias_kind":"pith_short_16","alias_value":"YPSCVTMT3SJLBLCI","created_at":"2026-07-05T09:01:30.189607+00:00"},{"alias_kind":"pith_short_8","alias_value":"YPSCVTMT","created_at":"2026-07-05T09:01:30.189607+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28641","citing_title":"Revisiting local regression: shape regularity, uniform rates, and the limits of random splits","ref_index":139,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YPSCVTMT3SJLBLCI5NOJHXIS6J","json":"https://pith.science/pith/YPSCVTMT3SJLBLCI5NOJHXIS6J.json","graph_json":"https://pith.science/api/pith-number/YPSCVTMT3SJLBLCI5NOJHXIS6J/graph.json","events_json":"https://pith.science/api/pith-number/YPSCVTMT3SJLBLCI5NOJHXIS6J/events.json","paper":"https://pith.science/paper/YPSCVTMT"},"agent_actions":{"view_html":"https://pith.science/pith/YPSCVTMT3SJLBLCI5NOJHXIS6J","download_json":"https://pith.science/pith/YPSCVTMT3SJLBLCI5NOJHXIS6J.json","view_paper":"https://pith.science/paper/YPSCVTMT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.11228&json=true","fetch_graph":"https://pith.science/api/pith-number/YPSCVTMT3SJLBLCI5NOJHXIS6J/graph.json","fetch_events":"https://pith.science/api/pith-number/YPSCVTMT3SJLBLCI5NOJHXIS6J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YPSCVTMT3SJLBLCI5NOJHXIS6J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YPSCVTMT3SJLBLCI5NOJHXIS6J/action/storage_attestation","attest_author":"https://pith.science/pith/YPSCVTMT3SJLBLCI5NOJHXIS6J/action/author_attestation","sign_citation":"https://pith.science/pith/YPSCVTMT3SJLBLCI5NOJHXIS6J/action/citation_signature","submit_replication":"https://pith.science/pith/YPSCVTMT3SJLBLCI5NOJHXIS6J/action/replication_record"}},"created_at":"2026-07-05T09:01:30.189607+00:00","updated_at":"2026-07-05T09:01:30.189607+00:00"}