{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FY7EHTSXZA77BV6F76WBOI7L7A","short_pith_number":"pith:FY7EHTSX","schema_version":"1.0","canonical_sha256":"2e3e43ce57c83ff0d7c5ffac1723ebf8190ff4eb58d49069300a726d1cd8eafe","source":{"kind":"arxiv","id":"2505.11143","version":2},"attestation_state":"computed","paper":{"title":"Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"William R.P. Denault","submitted_at":"2025-05-16T11:43:01Z","abstract_excerpt":"Sparse linear regression is a fundamental tool in data analysis. However, traditional approaches often fall short when covariates exhibit structure or arise from heterogeneous sources. In biomedical applications, covariates may stem from distinct modalities or be structured according to an underlying graph. We introduce \\textit{Neural Adaptive Shrinkage} (Nash), a unified framework that integrates covariate-specific side information into sparse regression via neural networks. Nash adaptively modulates penalties on a per-covariate basis, learning to tailor regularization without cross-validatio"},"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":"2505.11143","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-05-16T11:43:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c20240df57ac4ec8c3caedc2e910ff271181bafcadccc21fce52173103cdc415","abstract_canon_sha256":"be6357520f215fba83b6c460ad78e49ccc1e127cbe06bf3d51699ed1fb11324c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:09.518867Z","signature_b64":"Js4gI+NF/uxG5n1SdecSw7IXhJZLKHM5aFFg+/w3H9PuFtRTqOPLTpL54WkH5/NOyprDRNCr1h0SqIgx6Xj9CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e3e43ce57c83ff0d7c5ffac1723ebf8190ff4eb58d49069300a726d1cd8eafe","last_reissued_at":"2026-05-20T00:04:09.518192Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:09.518192Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"William R.P. Denault","submitted_at":"2025-05-16T11:43:01Z","abstract_excerpt":"Sparse linear regression is a fundamental tool in data analysis. However, traditional approaches often fall short when covariates exhibit structure or arise from heterogeneous sources. In biomedical applications, covariates may stem from distinct modalities or be structured according to an underlying graph. We introduce \\textit{Neural Adaptive Shrinkage} (Nash), a unified framework that integrates covariate-specific side information into sparse regression via neural networks. Nash adaptively modulates penalties on a per-covariate basis, learning to tailor regularization without cross-validatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11143","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/2505.11143/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":"2505.11143","created_at":"2026-05-20T00:04:09.518300+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.11143v2","created_at":"2026-05-20T00:04:09.518300+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11143","created_at":"2026-05-20T00:04:09.518300+00:00"},{"alias_kind":"pith_short_12","alias_value":"FY7EHTSXZA77","created_at":"2026-05-20T00:04:09.518300+00:00"},{"alias_kind":"pith_short_16","alias_value":"FY7EHTSXZA77BV6F","created_at":"2026-05-20T00:04:09.518300+00:00"},{"alias_kind":"pith_short_8","alias_value":"FY7EHTSX","created_at":"2026-05-20T00:04:09.518300+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/FY7EHTSXZA77BV6F76WBOI7L7A","json":"https://pith.science/pith/FY7EHTSXZA77BV6F76WBOI7L7A.json","graph_json":"https://pith.science/api/pith-number/FY7EHTSXZA77BV6F76WBOI7L7A/graph.json","events_json":"https://pith.science/api/pith-number/FY7EHTSXZA77BV6F76WBOI7L7A/events.json","paper":"https://pith.science/paper/FY7EHTSX"},"agent_actions":{"view_html":"https://pith.science/pith/FY7EHTSXZA77BV6F76WBOI7L7A","download_json":"https://pith.science/pith/FY7EHTSXZA77BV6F76WBOI7L7A.json","view_paper":"https://pith.science/paper/FY7EHTSX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.11143&json=true","fetch_graph":"https://pith.science/api/pith-number/FY7EHTSXZA77BV6F76WBOI7L7A/graph.json","fetch_events":"https://pith.science/api/pith-number/FY7EHTSXZA77BV6F76WBOI7L7A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FY7EHTSXZA77BV6F76WBOI7L7A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FY7EHTSXZA77BV6F76WBOI7L7A/action/storage_attestation","attest_author":"https://pith.science/pith/FY7EHTSXZA77BV6F76WBOI7L7A/action/author_attestation","sign_citation":"https://pith.science/pith/FY7EHTSXZA77BV6F76WBOI7L7A/action/citation_signature","submit_replication":"https://pith.science/pith/FY7EHTSXZA77BV6F76WBOI7L7A/action/replication_record"}},"created_at":"2026-05-20T00:04:09.518300+00:00","updated_at":"2026-05-20T00:04:09.518300+00:00"}