{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:UNQ2VCI25YFQ5K54L67MPJNNDM","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":"64de35d2df25956e22ab1a4bc205a9e2bd550512e8d8bf664109d33f14422f88","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-17T16:48:51Z","title_canon_sha256":"d86ccf708a4b43ae92b62ee09fc6db49a5bfa96f92aeefea0fc56f908eef6875"},"schema_version":"1.0","source":{"id":"2410.13749","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.13749","created_at":"2026-07-05T10:01:17Z"},{"alias_kind":"arxiv_version","alias_value":"2410.13749v2","created_at":"2026-07-05T10:01:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.13749","created_at":"2026-07-05T10:01:17Z"},{"alias_kind":"pith_short_12","alias_value":"UNQ2VCI25YFQ","created_at":"2026-07-05T10:01:17Z"},{"alias_kind":"pith_short_16","alias_value":"UNQ2VCI25YFQ5K54","created_at":"2026-07-05T10:01:17Z"},{"alias_kind":"pith_short_8","alias_value":"UNQ2VCI2","created_at":"2026-07-05T10:01:17Z"}],"graph_snapshots":[{"event_id":"sha256:bd5c76c6fdb1b0e2cc6a0d16f3a53a83a12b25f9008cc19edb7b7fed6e25f1f6","target":"graph","created_at":"2026-07-05T10:01:17Z","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/2410.13749/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The kernel thinning algorithm of Dwivedi & Mackey (2024) provides a better-than-i.i.d. compression of a generic set of points. By generating high-fidelity coresets of size significantly smaller than the input points, KT is known to speed up unsupervised tasks like Monte Carlo integration, uncertainty quantification, and non-parametric hypothesis testing, with minimal loss in statistical accuracy. In this work, we generalize the KT algorithm to speed up supervised learning problems involving kernel methods. Specifically, we combine two classical algorithms--Nadaraya-Watson (NW) regression or ke","authors_text":"Albert Gong, Kyuseong Choi, Raaz Dwivedi","cross_cats":["math.ST","stat.ML","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-17T16:48:51Z","title":"Supervised Kernel Thinning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.13749","kind":"arxiv","version":2},"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":"sha256:462405a690610de38c5d055e659305f7bade5bb8e728de49167e79f014996bf2","target":"record","created_at":"2026-07-05T10:01:17Z","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":"64de35d2df25956e22ab1a4bc205a9e2bd550512e8d8bf664109d33f14422f88","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-10-17T16:48:51Z","title_canon_sha256":"d86ccf708a4b43ae92b62ee09fc6db49a5bfa96f92aeefea0fc56f908eef6875"},"schema_version":"1.0","source":{"id":"2410.13749","kind":"arxiv","version":2}},"canonical_sha256":"a361aa891aee0b0eabbc5fbec7a5ad1b1e2ae22872b63f66ee8dabbd3f457272","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a361aa891aee0b0eabbc5fbec7a5ad1b1e2ae22872b63f66ee8dabbd3f457272","first_computed_at":"2026-07-05T10:01:17.067089Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:01:17.067089Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9u47z4n5ROObLSEs2MVjZMvOkvQ3VfSAfBhWWioSDPgXwZeqyhlhXTv+Ny5KVsHEY/rQL4zjfRSn3Yi/UZenCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:01:17.067666Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.13749","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:462405a690610de38c5d055e659305f7bade5bb8e728de49167e79f014996bf2","sha256:bd5c76c6fdb1b0e2cc6a0d16f3a53a83a12b25f9008cc19edb7b7fed6e25f1f6"],"state_sha256":"1fd7548e72d920d9dabc35ef67901f337aef0aadffe9711f08d0f6463fdbe1d5"}