{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:W2DACR56BRIWZYDTXS3NOK7J2X","short_pith_number":"pith:W2DACR56","schema_version":"1.0","canonical_sha256":"b6860147be0c516ce073bcb6d72be9d5d654629b5f197cd626d05153bdfc9d6c","source":{"kind":"arxiv","id":"1910.08892","version":3},"attestation_state":"computed","paper":{"title":"Bayesian Symbolic Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jiadong Guo, Jian Guo, Jian Kang, Weilin Fu, Ying Jin","submitted_at":"2019-10-20T04:28:50Z","abstract_excerpt":"Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machine learning method by bridging X and Y using mathematical expressions composed of some basic functions. However, the search space of all possible expressions grows exponentially with the length of the expression, making it infeasible for enumeration. Genetic programming (GP) has been traditionally and commonly used in SR to search for the optimal solution, but it suffers from several limitations, e.g. the difficulty "},"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":"1910.08892","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-10-20T04:28:50Z","cross_cats_sorted":[],"title_canon_sha256":"d384901ca1f13d06a30d8638f819f9b14e0a518a93ca0214b04cc715246b6363","abstract_canon_sha256":"2c4e2e553642f3e900929cf7ee2f71f557ea10ae68f1f3c4bd3d544dd4fde642"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:33:44.481634Z","signature_b64":"uDyKFokqUfxcPJhjml37By3N+azqiDyJ1iWRRbulBtfETyrKay4w6u/lYI6uwREtB91SxnBUpvcaPnQQPp2zDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6860147be0c516ce073bcb6d72be9d5d654629b5f197cd626d05153bdfc9d6c","last_reissued_at":"2026-07-05T00:33:44.481021Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:33:44.481021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Symbolic Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jiadong Guo, Jian Guo, Jian Kang, Weilin Fu, Ying Jin","submitted_at":"2019-10-20T04:28:50Z","abstract_excerpt":"Interpretability is crucial for machine learning in many scenarios such as quantitative finance, banking, healthcare, etc. Symbolic regression (SR) is a classic interpretable machine learning method by bridging X and Y using mathematical expressions composed of some basic functions. However, the search space of all possible expressions grows exponentially with the length of the expression, making it infeasible for enumeration. Genetic programming (GP) has been traditionally and commonly used in SR to search for the optimal solution, but it suffers from several limitations, e.g. the difficulty "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.08892","kind":"arxiv","version":3},"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/1910.08892/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":"1910.08892","created_at":"2026-07-05T00:33:44.481091+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.08892v3","created_at":"2026-07-05T00:33:44.481091+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.08892","created_at":"2026-07-05T00:33:44.481091+00:00"},{"alias_kind":"pith_short_12","alias_value":"W2DACR56BRIW","created_at":"2026-07-05T00:33:44.481091+00:00"},{"alias_kind":"pith_short_16","alias_value":"W2DACR56BRIWZYDT","created_at":"2026-07-05T00:33:44.481091+00:00"},{"alias_kind":"pith_short_8","alias_value":"W2DACR56","created_at":"2026-07-05T00:33:44.481091+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07915","citing_title":"EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification","ref_index":73,"is_internal_anchor":false},{"citing_arxiv_id":"2410.17448","citing_title":"In Context Learning and Reasoning for Symbolic Regression with Large Language Models","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2501.05684","citing_title":"Distilling human mobility models with symbolic regression","ref_index":54,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21813","citing_title":"Symbolic Density Estimation for Discrete Distributions","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20408","citing_title":"Spectral Souping: A Unified Framework for Online Preference Alignment","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01067","citing_title":"Deep Variational Inference Symbolic Regression","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23236","citing_title":"The functional form of galaxy and halo luminosity and mass functions","ref_index":39,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W2DACR56BRIWZYDTXS3NOK7J2X","json":"https://pith.science/pith/W2DACR56BRIWZYDTXS3NOK7J2X.json","graph_json":"https://pith.science/api/pith-number/W2DACR56BRIWZYDTXS3NOK7J2X/graph.json","events_json":"https://pith.science/api/pith-number/W2DACR56BRIWZYDTXS3NOK7J2X/events.json","paper":"https://pith.science/paper/W2DACR56"},"agent_actions":{"view_html":"https://pith.science/pith/W2DACR56BRIWZYDTXS3NOK7J2X","download_json":"https://pith.science/pith/W2DACR56BRIWZYDTXS3NOK7J2X.json","view_paper":"https://pith.science/paper/W2DACR56","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.08892&json=true","fetch_graph":"https://pith.science/api/pith-number/W2DACR56BRIWZYDTXS3NOK7J2X/graph.json","fetch_events":"https://pith.science/api/pith-number/W2DACR56BRIWZYDTXS3NOK7J2X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W2DACR56BRIWZYDTXS3NOK7J2X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W2DACR56BRIWZYDTXS3NOK7J2X/action/storage_attestation","attest_author":"https://pith.science/pith/W2DACR56BRIWZYDTXS3NOK7J2X/action/author_attestation","sign_citation":"https://pith.science/pith/W2DACR56BRIWZYDTXS3NOK7J2X/action/citation_signature","submit_replication":"https://pith.science/pith/W2DACR56BRIWZYDTXS3NOK7J2X/action/replication_record"}},"created_at":"2026-07-05T00:33:44.481091+00:00","updated_at":"2026-07-05T00:33:44.481091+00:00"}