{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ISOLEGART2ROWQ2KQQV2U7ZZGF","short_pith_number":"pith:ISOLEGAR","schema_version":"1.0","canonical_sha256":"449cb218119ea2eb434a842baa7f393154ea1a2a1a5fbc1bdc6294319233a20c","source":{"kind":"arxiv","id":"2505.09075","version":3},"attestation_state":"computed","paper":{"title":"Risk Bounds For Distributional Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla, Sabyasachi Chatterjee","submitted_at":"2025-05-14T02:22:12Z","abstract_excerpt":"This work examines risk bounds for nonparametric distributional regression estimators. For convex-constrained distributional regression, general upper bounds are established for the continuous ranked probability score (CRPS) and the worst-case mean squared error (MSE) across the domain. These theoretical results are applied to isotonic and trend filtering distributional regression, yielding convergence rates consistent with those for mean estimation. Furthermore, a general upper bound is derived for distributional regression under non-convex constraints, with a specific application to neural n"},"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":"2505.09075","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-05-14T02:22:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"71b10a6c2ee3001d57c8bd42a22f40d4d808d197ff211720b3e68c9a9d61175c","abstract_canon_sha256":"a714bf5a974deaad249ea28aca7b0e297d4dcdf24bdf824d7798371987bdb73a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:55.952815Z","signature_b64":"2ozQw/sLa30mwV2Cwj4V3RfqYuURESirKyqi4swfIU/fRj6G3FYHBg/7RHth+pT+oYu/xJl52bupkzIp3po4Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"449cb218119ea2eb434a842baa7f393154ea1a2a1a5fbc1bdc6294319233a20c","last_reissued_at":"2026-07-05T11:35:55.952335Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:55.952335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Risk Bounds For Distributional Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla, Sabyasachi Chatterjee","submitted_at":"2025-05-14T02:22:12Z","abstract_excerpt":"This work examines risk bounds for nonparametric distributional regression estimators. For convex-constrained distributional regression, general upper bounds are established for the continuous ranked probability score (CRPS) and the worst-case mean squared error (MSE) across the domain. These theoretical results are applied to isotonic and trend filtering distributional regression, yielding convergence rates consistent with those for mean estimation. Furthermore, a general upper bound is derived for distributional regression under non-convex constraints, with a specific application to neural n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09075","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/2505.09075/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":"2505.09075","created_at":"2026-07-05T11:35:55.952399+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.09075v3","created_at":"2026-07-05T11:35:55.952399+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09075","created_at":"2026-07-05T11:35:55.952399+00:00"},{"alias_kind":"pith_short_12","alias_value":"ISOLEGART2RO","created_at":"2026-07-05T11:35:55.952399+00:00"},{"alias_kind":"pith_short_16","alias_value":"ISOLEGART2ROWQ2K","created_at":"2026-07-05T11:35:55.952399+00:00"},{"alias_kind":"pith_short_8","alias_value":"ISOLEGAR","created_at":"2026-07-05T11:35:55.952399+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/ISOLEGART2ROWQ2KQQV2U7ZZGF","json":"https://pith.science/pith/ISOLEGART2ROWQ2KQQV2U7ZZGF.json","graph_json":"https://pith.science/api/pith-number/ISOLEGART2ROWQ2KQQV2U7ZZGF/graph.json","events_json":"https://pith.science/api/pith-number/ISOLEGART2ROWQ2KQQV2U7ZZGF/events.json","paper":"https://pith.science/paper/ISOLEGAR"},"agent_actions":{"view_html":"https://pith.science/pith/ISOLEGART2ROWQ2KQQV2U7ZZGF","download_json":"https://pith.science/pith/ISOLEGART2ROWQ2KQQV2U7ZZGF.json","view_paper":"https://pith.science/paper/ISOLEGAR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.09075&json=true","fetch_graph":"https://pith.science/api/pith-number/ISOLEGART2ROWQ2KQQV2U7ZZGF/graph.json","fetch_events":"https://pith.science/api/pith-number/ISOLEGART2ROWQ2KQQV2U7ZZGF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ISOLEGART2ROWQ2KQQV2U7ZZGF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ISOLEGART2ROWQ2KQQV2U7ZZGF/action/storage_attestation","attest_author":"https://pith.science/pith/ISOLEGART2ROWQ2KQQV2U7ZZGF/action/author_attestation","sign_citation":"https://pith.science/pith/ISOLEGART2ROWQ2KQQV2U7ZZGF/action/citation_signature","submit_replication":"https://pith.science/pith/ISOLEGART2ROWQ2KQQV2U7ZZGF/action/replication_record"}},"created_at":"2026-07-05T11:35:55.952399+00:00","updated_at":"2026-07-05T11:35:55.952399+00:00"}