{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:R3LPX4CGGDEHOUIMIZKE7O65H4","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":"90fdb016dbfcdd06cfc7f9d8a6d96e7d106f0afddecab2031010d34d17bc6c5b","cross_cats_sorted":["cs.CG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-16T10:24:03Z","title_canon_sha256":"31caa1c46a86710bc878c9cb6bd0ee764de66f4a1654137d6862f7a31359e7cf"},"schema_version":"1.0","source":{"id":"2206.08061","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.08061","created_at":"2026-07-05T04:32:21Z"},{"alias_kind":"arxiv_version","alias_value":"2206.08061v1","created_at":"2026-07-05T04:32:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.08061","created_at":"2026-07-05T04:32:21Z"},{"alias_kind":"pith_short_12","alias_value":"R3LPX4CGGDEH","created_at":"2026-07-05T04:32:21Z"},{"alias_kind":"pith_short_16","alias_value":"R3LPX4CGGDEHOUIM","created_at":"2026-07-05T04:32:21Z"},{"alias_kind":"pith_short_8","alias_value":"R3LPX4CG","created_at":"2026-07-05T04:32:21Z"}],"graph_snapshots":[{"event_id":"sha256:b54ca7bde154df9cfafdc595f43c1369f3e7245aa821b3f1947fafbdd9303eee","target":"graph","created_at":"2026-07-05T04:32:21Z","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/2206.08061/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce an algorithm for active function approximation based on nearest neighbor regression. Our Active Nearest Neighbor Regressor (ANNR) relies on the Voronoi-Delaunay framework from computational geometry to subdivide the space into cells with constant estimated function value and select novel query points in a way that takes the geometry of the function graph into account. We consider the recent state-of-the-art active function approximator called DEFER, which is based on incremental rectangular partitioning of the space, as the main baseline. The ANNR addresses a number of limitations","authors_text":"Alexander Kravberg, Anastasiia Varava, Danica Kragic, Florian T. Pokorny, Giovanni Luca Marchetti, Vladislav Polianskii","cross_cats":["cs.CG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-16T10:24:03Z","title":"Active Nearest Neighbor Regression Through Delaunay Refinement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.08061","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":"sha256:198ef7c7167469bf1b9870efde4d7975cd9a44f8c422eb71b849daa738f22e83","target":"record","created_at":"2026-07-05T04:32:21Z","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":"90fdb016dbfcdd06cfc7f9d8a6d96e7d106f0afddecab2031010d34d17bc6c5b","cross_cats_sorted":["cs.CG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-06-16T10:24:03Z","title_canon_sha256":"31caa1c46a86710bc878c9cb6bd0ee764de66f4a1654137d6862f7a31359e7cf"},"schema_version":"1.0","source":{"id":"2206.08061","kind":"arxiv","version":1}},"canonical_sha256":"8ed6fbf04630c877510c46544fbbdd3f1fc8d155aeede38b59e43e03212e7798","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8ed6fbf04630c877510c46544fbbdd3f1fc8d155aeede38b59e43e03212e7798","first_computed_at":"2026-07-05T04:32:21.694188Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:32:21.694188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qf53PpU7ml43jtrmXtWjHC6g2bvJVNeLIgyXGEULifePST6xYAlYi7bxPucNTkFl7cj/nT1Uu5gGQCibka1UDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:32:21.694703Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.08061","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:198ef7c7167469bf1b9870efde4d7975cd9a44f8c422eb71b849daa738f22e83","sha256:b54ca7bde154df9cfafdc595f43c1369f3e7245aa821b3f1947fafbdd9303eee"],"state_sha256":"bdbbc4766698d1b5739d5c2ebbf6fe7782b91097e97ed49ca55a8cd1097803ee"}