{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:KKDCB6UBDCDTYDGUEGOMEZT23A","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":"1e6497bc32d27c6fe788143d6d3f9ac0e9711bd3b9f758e3fc11e96ebe373114","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-22T23:09:27Z","title_canon_sha256":"63e5035ec9c145f698cc31c6f123d5bc51e8110afa3f51084693c2bb29f77141"},"schema_version":"1.0","source":{"id":"1908.08619","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.08619","created_at":"2026-07-05T00:51:03Z"},{"alias_kind":"arxiv_version","alias_value":"1908.08619v4","created_at":"2026-07-05T00:51:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08619","created_at":"2026-07-05T00:51:03Z"},{"alias_kind":"pith_short_12","alias_value":"KKDCB6UBDCDT","created_at":"2026-07-05T00:51:03Z"},{"alias_kind":"pith_short_16","alias_value":"KKDCB6UBDCDTYDGU","created_at":"2026-07-05T00:51:03Z"},{"alias_kind":"pith_short_8","alias_value":"KKDCB6UB","created_at":"2026-07-05T00:51:03Z"}],"graph_snapshots":[{"event_id":"sha256:d9d2b3777a89b14b06c4d4611c08ddfed1c1e20ce54957c9fcefc33a7192d386","target":"graph","created_at":"2026-07-05T00:51:03Z","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/1908.08619/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Given a data set $\\mathcal{D}$ containing millions of data points and a data consumer who is willing to pay for \\$$X$ to train a machine learning (ML) model over $\\mathcal{D}$, how should we distribute this \\$$X$ to each data point to reflect its \"value\"? In this paper, we define the \"relative value of data\" via the Shapley value, as it uniquely possesses properties with appealing real-world interpretations, such as fairness, rationality and decentralizability. For general, bounded utility functions, the Shapley value is known to be challenging to compute: to get Shapley values for all $N$ dat","authors_text":"Bo Li, Boxin Wang, Ce Zhang, Costas J. Spanos, David Dao, Dawn Song, Frances Ann Hubis, Nezihe Merve Gurel, Ruoxi Jia","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-22T23:09:27Z","title":"Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08619","kind":"arxiv","version":4},"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":"sha256:31c8ecc76459987f01bd18417018fca644d868e1913e1e22ff82064b9745f6a6","target":"record","created_at":"2026-07-05T00:51:03Z","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":"1e6497bc32d27c6fe788143d6d3f9ac0e9711bd3b9f758e3fc11e96ebe373114","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-22T23:09:27Z","title_canon_sha256":"63e5035ec9c145f698cc31c6f123d5bc51e8110afa3f51084693c2bb29f77141"},"schema_version":"1.0","source":{"id":"1908.08619","kind":"arxiv","version":4}},"canonical_sha256":"528620fa8118873c0cd4219cc2667ad8362730c8fa06850a8d8f6d1f19a5b87e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"528620fa8118873c0cd4219cc2667ad8362730c8fa06850a8d8f6d1f19a5b87e","first_computed_at":"2026-07-05T00:51:03.629036Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:51:03.629036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"On0fN5cyahRuHj5Gpel+X0tCpLU1Nso+8KuXry/uC4T7PnhvDwNVwlvJABSiWbEe8tv/i8HpcQUNa31rkXkvBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:51:03.629435Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.08619","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:31c8ecc76459987f01bd18417018fca644d868e1913e1e22ff82064b9745f6a6","sha256:d9d2b3777a89b14b06c4d4611c08ddfed1c1e20ce54957c9fcefc33a7192d386"],"state_sha256":"f9a540db14f1fd7927aa98d94af1cb9e0fa4156773588c18a2078f100b575e06"}