{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:MVAYRJEMR642DRIQQGUENI6YN5","short_pith_number":"pith:MVAYRJEM","schema_version":"1.0","canonical_sha256":"654188a48c8fb9a1c51081a846a3d86f6fc1765066d5d869d3ae0f4bcf6086c2","source":{"kind":"arxiv","id":"1909.11720","version":1},"attestation_state":"computed","paper":{"title":"Benefit of Interpolation in Nearest Neighbor Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Guang Cheng, Qifan Song, Yue Xing","submitted_at":"2019-09-25T19:24:24Z","abstract_excerpt":"The over-parameterized models attract much attention in the era of data science and deep learning. It is empirically observed that although these models, e.g. deep neural networks, over-fit the training data, they can still achieve small testing error, and sometimes even {\\em outperform} traditional algorithms which are designed to avoid over-fitting. The major goal of this work is to sharply quantify the benefit of data interpolation in the context of nearest neighbors (NN) algorithm. Specifically, we consider a class of interpolated weighting schemes and then carefully characterize their asy"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1909.11720","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-09-25T19:24:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0cd0f399353a7022fc6ee911b56b8aa20d234b2d969d7452b7d51a9b172712f2","abstract_canon_sha256":"b744e063f1b0c2eac5e017893d2fbd039aad535c4405a0705a1c26cb98b5cf2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:07:27.310049Z","signature_b64":"dByTQeJJxsCxuGsRZspf5lNF2S4Ya8IK+7du4j9B5CvIbiHWGNy0aK1fEhR+qHya8NzNJ/bWajmbOewHiqf5AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"654188a48c8fb9a1c51081a846a3d86f6fc1765066d5d869d3ae0f4bcf6086c2","last_reissued_at":"2026-07-05T00:07:27.309622Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:07:27.309622Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benefit of Interpolation in Nearest Neighbor Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Guang Cheng, Qifan Song, Yue Xing","submitted_at":"2019-09-25T19:24:24Z","abstract_excerpt":"The over-parameterized models attract much attention in the era of data science and deep learning. It is empirically observed that although these models, e.g. deep neural networks, over-fit the training data, they can still achieve small testing error, and sometimes even {\\em outperform} traditional algorithms which are designed to avoid over-fitting. The major goal of this work is to sharply quantify the benefit of data interpolation in the context of nearest neighbors (NN) algorithm. Specifically, we consider a class of interpolated weighting schemes and then carefully characterize their asy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.11720","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1909.11720/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1909.11720","created_at":"2026-07-05T00:07:27.309681+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.11720v1","created_at":"2026-07-05T00:07:27.309681+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.11720","created_at":"2026-07-05T00:07:27.309681+00:00"},{"alias_kind":"pith_short_12","alias_value":"MVAYRJEMR642","created_at":"2026-07-05T00:07:27.309681+00:00"},{"alias_kind":"pith_short_16","alias_value":"MVAYRJEMR642DRIQ","created_at":"2026-07-05T00:07:27.309681+00:00"},{"alias_kind":"pith_short_8","alias_value":"MVAYRJEM","created_at":"2026-07-05T00:07:27.309681+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MVAYRJEMR642DRIQQGUENI6YN5","json":"https://pith.science/pith/MVAYRJEMR642DRIQQGUENI6YN5.json","graph_json":"https://pith.science/api/pith-number/MVAYRJEMR642DRIQQGUENI6YN5/graph.json","events_json":"https://pith.science/api/pith-number/MVAYRJEMR642DRIQQGUENI6YN5/events.json","paper":"https://pith.science/paper/MVAYRJEM"},"agent_actions":{"view_html":"https://pith.science/pith/MVAYRJEMR642DRIQQGUENI6YN5","download_json":"https://pith.science/pith/MVAYRJEMR642DRIQQGUENI6YN5.json","view_paper":"https://pith.science/paper/MVAYRJEM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.11720&json=true","fetch_graph":"https://pith.science/api/pith-number/MVAYRJEMR642DRIQQGUENI6YN5/graph.json","fetch_events":"https://pith.science/api/pith-number/MVAYRJEMR642DRIQQGUENI6YN5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MVAYRJEMR642DRIQQGUENI6YN5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MVAYRJEMR642DRIQQGUENI6YN5/action/storage_attestation","attest_author":"https://pith.science/pith/MVAYRJEMR642DRIQQGUENI6YN5/action/author_attestation","sign_citation":"https://pith.science/pith/MVAYRJEMR642DRIQQGUENI6YN5/action/citation_signature","submit_replication":"https://pith.science/pith/MVAYRJEMR642DRIQQGUENI6YN5/action/replication_record"}},"created_at":"2026-07-05T00:07:27.309681+00:00","updated_at":"2026-07-05T00:07:27.309681+00:00"}