{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ERXALBJGTITZTJMUW3BPO4QSQE","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":"31bcacca0cee601c30214ccee525555aca94ecf0ef8e9e861108c6c4937c2b36","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-04-16T10:30:52Z","title_canon_sha256":"d94058c0640d50fc3f80b7d7ec417578d4c030ec085f8363e31c9f3ed8c8c809"},"schema_version":"1.0","source":{"id":"2404.10444","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.10444","created_at":"2026-07-05T08:08:30Z"},{"alias_kind":"arxiv_version","alias_value":"2404.10444v1","created_at":"2026-07-05T08:08:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.10444","created_at":"2026-07-05T08:08:30Z"},{"alias_kind":"pith_short_12","alias_value":"ERXALBJGTITZ","created_at":"2026-07-05T08:08:30Z"},{"alias_kind":"pith_short_16","alias_value":"ERXALBJGTITZTJMU","created_at":"2026-07-05T08:08:30Z"},{"alias_kind":"pith_short_8","alias_value":"ERXALBJG","created_at":"2026-07-05T08:08:30Z"}],"graph_snapshots":[{"event_id":"sha256:f6f6125b1f6328040e89ccd537dee8dd1a1b8e14d6644491c7eed6a86e955996","target":"graph","created_at":"2026-07-05T08:08:30Z","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/2404.10444/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper explores the field of semi-supervised Fr\\'echet regression, driven by the significant costs associated with obtaining non-Euclidean labels. Methodologically, we propose two novel methods: semi-supervised NW Fr\\'echet regression and semi-supervised kNN Fr\\'echet regression, both based on graph distance acquired from all feature instances. These methods extend the scope of existing semi-supervised Euclidean regression methods. We establish their convergence rates with limited labeled data and large amounts of unlabeled data, taking into account the low-dimensional manifold structure o","authors_text":"Rui Qiu, Zhenhua Lin, Zhou Yu","cross_cats":["cs.LG","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-04-16T10:30:52Z","title":"Semi-supervised Fr\\'echet Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.10444","kind":"arxiv","version":1},"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:ae922977c28aacf706d2fe6c2d0662675e546beb5b47392c6945acb0c05a7723","target":"record","created_at":"2026-07-05T08:08:30Z","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":"31bcacca0cee601c30214ccee525555aca94ecf0ef8e9e861108c6c4937c2b36","cross_cats_sorted":["cs.LG","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2024-04-16T10:30:52Z","title_canon_sha256":"d94058c0640d50fc3f80b7d7ec417578d4c030ec085f8363e31c9f3ed8c8c809"},"schema_version":"1.0","source":{"id":"2404.10444","kind":"arxiv","version":1}},"canonical_sha256":"246e0585269a2799a594b6c2f77212813bb9ecc26a1d8ff8a5c3a6f637fa2d11","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"246e0585269a2799a594b6c2f77212813bb9ecc26a1d8ff8a5c3a6f637fa2d11","first_computed_at":"2026-07-05T08:08:30.584617Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:08:30.584617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ogXTnZEvdKj18saUkqMKZmiG9m/89ffimISlawONImXI/R3RDKJt+CeAnUCSLHvA8jARXjU0UW7Gyxf7k2CuCw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:08:30.585046Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.10444","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ae922977c28aacf706d2fe6c2d0662675e546beb5b47392c6945acb0c05a7723","sha256:f6f6125b1f6328040e89ccd537dee8dd1a1b8e14d6644491c7eed6a86e955996"],"state_sha256":"43a2d270aaa916d653243049317b57d4ba3abec9af89051b9f3a7f18e6139fee"}