{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YFH3M5VCWRYTDNMERUMUUMEHJE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"189cd020923af89bdfccc0f80f20aa13068813a53aa8269a8f9d66cb6a5877d0","cross_cats_sorted":["cs.NA","math.NA","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-02-24T21:37:54Z","title_canon_sha256":"2a19037a1f1baed1558548c4bfb812d254ff803232d7ca2db28847994a80cb34"},"schema_version":"1.0","source":{"id":"2502.17671","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.17671","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"arxiv_version","alias_value":"2502.17671v3","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.17671","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"pith_short_12","alias_value":"YFH3M5VCWRYT","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"pith_short_16","alias_value":"YFH3M5VCWRYTDNME","created_at":"2026-07-05T11:11:42Z"},{"alias_kind":"pith_short_8","alias_value":"YFH3M5VC","created_at":"2026-07-05T11:11:42Z"}],"graph_snapshots":[{"event_id":"sha256:950e0cc377a11ae082df197bee3040ce7c9df6ae0fd7c53a5e09132e29de0d4f","target":"graph","created_at":"2026-07-05T11:11:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.17671/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A fundamental problem in statistics and machine learning is to estimate a function $f$ from possibly noisy observations of its point samples. The goal is to design a numerical algorithm to construct an approximation $\\hat f$ to $f$ in a prescribed norm that asymptotically achieves the best possible error (as a function of the number $m$ of observations and the variance $\\sigma^2$ of the noise). This problem has received considerable attention in both nonparametric statistics (noisy observations) and optimal recovery (noiseless observations). Quantitative bounds require assumptions on $f$, know","authors_text":"Guergana Petrova, Jonathan W. Siegel, Rahul Parhi, Robert D. Nowak, Ronald DeVore","cross_cats":["cs.NA","math.NA","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-02-24T21:37:54Z","title":"Optimal Recovery Meets Minimax Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.17671","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f8f59d2d2ad720afd2d390d7b1c20b53aa1bcc8faedf55ab238d2813853e9a33","target":"record","created_at":"2026-07-05T11:11:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"189cd020923af89bdfccc0f80f20aa13068813a53aa8269a8f9d66cb6a5877d0","cross_cats_sorted":["cs.NA","math.NA","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-02-24T21:37:54Z","title_canon_sha256":"2a19037a1f1baed1558548c4bfb812d254ff803232d7ca2db28847994a80cb34"},"schema_version":"1.0","source":{"id":"2502.17671","kind":"arxiv","version":3}},"canonical_sha256":"c14fb676a2b47131b5848d194a30874913044aac8329887480dbb87e35f5ad87","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c14fb676a2b47131b5848d194a30874913044aac8329887480dbb87e35f5ad87","first_computed_at":"2026-07-05T11:11:42.528830Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:11:42.528830Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bXxd/nwoY7y2dBmI14NUfWrD4GypLOcr3fqc7IrjJCsgCx5ervKNN6O+IdA05ONnhjiFqi4Y8Eb0LNQQeP3NDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:11:42.529302Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.17671","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f8f59d2d2ad720afd2d390d7b1c20b53aa1bcc8faedf55ab238d2813853e9a33","sha256:950e0cc377a11ae082df197bee3040ce7c9df6ae0fd7c53a5e09132e29de0d4f"],"state_sha256":"bc2b38b39e166ef46b953adc5822b33e28d758329345449613fb9d77afdf9e88"}