{"as_of":"2026-08-20T21:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0acf317747b39fc055aef4cfc47cca4e57d8f1fc037f308823737c62f6417d7d","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T05:27:17.468595Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1909.01464/citation-record","integrity":"/paper/1909.01464/integrity","json":"/paper/1909.01464/citation-record.json","paper":"/paper/1909.01464"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.465595Z","title":"Optimizing parallel algorithms for all pairs similarity search,","venue":null,"work_id":"dd8277dc-e7e9-46b1-8dc0-d6bd6d8fd578","year":2013},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.198429Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:d37e9e6c0b8fb1a153eae97b69a5c6f55fbbb4ea1589f1177d46ef21c681a330","observation_id":"8f4c30be-53b2-4bbd-bdb8-d7af002918df","resolution":{"observed_at":"2026-08-14T05:27:18.472018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.446700Z","title":"Parallel cosine nearest neighbor graph construction,","venue":null,"work_id":"53a5b69f-142c-4fcb-b969-6c003f9feff6","year":2017},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.208570Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:608dbc588c288be358dfc45d91d2bdf3d572deb6141e90f28c40d01b52bbeb4e","observation_id":"a2d0632d-d4a6-47ba-9db8-13170fbd9d0e","resolution":{"observed_at":"2026-08-14T05:27:18.452037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.428605Z","title":"Fast learning rates for plug-in classiﬁers,","venue":null,"work_id":"942091b4-b727-471b-9052-e7fcddd78048","year":2007},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.215440Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:0d504456e462747ea60bb9973b6690a3872ac3f3218fa9873208c2bc86cdbe67","observation_id":"720a60ce-8ed2-4b02-b6c1-22db5f57a810","resolution":{"observed_at":"2026-08-14T05:27:18.434104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.408775Z","title":"Searching for exotic particles in high-energy physics with deep learning,","venue":null,"work_id":"4a198026-5ef5-4eb7-a2d2-61dea40f8769","year":2014},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.222438Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:156034c7232f16cb3c5af5d3265eeec33ff01c43d7b9882101f77e709be86fc5","observation_id":"c19d7f8b-4cc2-4b9f-a22b-120c2c8143ac","resolution":{"observed_at":"2026-08-14T05:27:18.414750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1509.05457","last_updated":"2015-09-17T22:08:41Z","snapshot_observed_at":"2026-08-14T22:31:53.502421Z","submitted_at":"2015-09-17T22:08:41Z","title":"Distributed Estimation and Inference with Statistical Guarantees","version":1},"cited_work":{"arxiv_id":"1509.05457","doi":null,"metadata_source":"pith","pith_arxiv_id":"1509.05457","snapshot_observed_at":"2026-08-14T05:27:17.546782Z","title":"Distributed Estimation and Inference with Statistical Guarantees","venue":"math.ST","work_id":"d0196840-a01b-4741-b89f-d1661da3c684","year":2015},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.227685Z"},"links":{"cited_paper":"/paper/1509.05457","citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:6feab52f589917e7632e6ca3ead6afbecd349f2f96a9a9d5a8137988648af4de","observation_id":"e28dda98-8a42-48e9-8f97-2c6e5fd9abfb","resolution":{"observed_at":"2026-08-14T05:27:17.552473Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.388704Z","title":"Multidimensional binary search trees used for associative searching,","venue":null,"work_id":"6ea975fc-3283-4287-aa2a-e420bb51a1c5","year":1975},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.234017Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:acd246e4a0e52f4db30d03bf2d64e351bd9d0c7d5c2191761f0b2ec440a59fc2","observation_id":"7deca1dc-54d6-4d68-b6ae-3a8331825f5f","resolution":{"observed_at":"2026-08-14T05:27:18.394604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.366959Z","title":"Bagging predictors,","venue":null,"work_id":"2837a3db-84e2-4ad0-8684-96f0f9bef3d3","year":1996},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.240573Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:2715ae5bf1b8e6e4389847be605b7834cbc0a1ebcda9ca331071acde0594b51f","observation_id":"7bb68709-c747-4485-9d7b-7e21c48b761c","resolution":{"observed_at":"2026-08-14T05:27:18.374003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.347204Z","title":"Accurate occupancy detection of an ofﬁce room from light, temperature, humidity and CO2 measurements using statistical learning models,","venue":null,"work_id":"05fd4810-d0e3-4631-9081-6096e7cc4140","year":2016},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.247860Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:6cbbb02e4ddb00eacfb54b577833fae7c758730704c9c95fd14b814d1915ea27","observation_id":"cd3b1316-3453-4d28-bf20-15c31a844429","resolution":{"observed_at":"2026-08-14T05:27:18.352799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.327020Z","title":"Rates of convergence for nearest neighbor classiﬁca- tion,","venue":null,"work_id":"4f2bf2a2-e90d-4f29-8f6f-97213667be60","year":2014},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.253159Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:8d7449365d760b6d0a606c8af7204eb4644755fe5aa1a91565abf2f590e5c889","observation_id":"7aa07fa3-e634-450a-b74d-b957c0e518e9","resolution":{"observed_at":"2026-08-14T05:27:18.334115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.308778Z","title":"Learning ensembles from bites: A scalable and accurate approach,","venue":null,"work_id":"08ac5392-36f6-4b72-a6a4-4b1e5ecad402","year":2004},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.258262Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:9ab9c2b20fab47c8e33403e0eed3450f96ebade26f2b3e2b01039162de3220d8","observation_id":"17715eae-f5a1-4bc4-af8a-f92ee13c3bca","resolution":{"observed_at":"2026-08-14T05:27:18.314595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.287957Z","title":"A split-and-conquer approach for analysis of extraordinarily large data,","venue":null,"work_id":"dd0c34a5-5e3c-4c32-9716-101c174827be","year":2014},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.263323Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:ba1033db110426beacf4ae132a442bcf6422c81f22bbe7aa014662e8b81c20ab","observation_id":"8e937822-3b80-4803-859f-3ec3f97b5fe3","resolution":{"observed_at":"2026-08-14T05:27:18.295427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.268049Z","title":"Nearest neighbor pattern classiﬁcation,","venue":null,"work_id":"3e5eebaa-6e58-4fc0-901e-76a731979ff5","year":1967},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.269712Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:10c78acdae941778332f5b38e029f0fa2372dcfed11a47b25bb7ee52a20f3404","observation_id":"1a236cd9-2c55-48cf-a4c0-4f507c39da8d","resolution":{"observed_at":"2026-08-14T05:27:18.274791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.248017Z","title":"Rates of convergence for nearest neighbor procedures,","venue":null,"work_id":"55cb8564-7865-42c5-b85f-142477de27f7","year":1968},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.275757Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:e7ee912705b941a5c265782173ad27576134d5b04882442ba41f3cd9f0d77031","observation_id":"6ae957f5-09fd-4ffc-936a-5b87151be0e6","resolution":{"observed_at":"2026-08-14T05:27:18.254045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.229498Z","title":"Randomized partition trees for exact nearest neighbor search,","venue":null,"work_id":"5192d815-3cb0-4a79-9e83-c461f58d15ae","year":2013},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.280941Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:4ae8c728c91c7987424829d37bdfb55b5e2abaf2647dff0fdaa34e58e3f24f7e","observation_id":"a2e40da4-affa-40bf-9c5a-75b454f68665","resolution":{"observed_at":"2026-08-14T05:27:18.235623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.210764Z","title":"On the strong universal consis- tency of nearest neighbor regression function estimates,","venue":null,"work_id":"1ad95ffb-9a5d-4da0-aa07-2251f6ce6619","year":1994},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.285626Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:48d04bf8dcbd11e5028d2182b24295b1e2a74fe447125295099651510bda8996","observation_id":"ff77d2c8-5aaa-4c17-9bfe-80796d1c7d2e","resolution":{"observed_at":"2026-08-14T05:27:18.216381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.187821Z","title":"A comparison of dynamic reposing and tangent distance for drug activity prediction,","venue":null,"work_id":"992996ec-bf38-45da-8f7f-127696a7e29b","year":1994},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.291271Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:58344c32a2b50aef1d671973e66236c894b3b60172027f7d3916fead64e82f92","observation_id":"53badf64-d7fe-4848-822a-9980a3b5468c","resolution":{"observed_at":"2026-08-14T05:27:18.196309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.164958Z","title":"Solving the multiple instance problem with axis-parallel rectangles,","venue":null,"work_id":"a005f23b-0272-4fa0-a6c4-5ad8bb268020","year":1997},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.297027Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:48cebfd8de28084a545bb2f358243ff873b8b53542c105ff59bf414e502ac3fc","observation_id":"01a64cc0-a248-41d7-b105-bb48c3e0b650","resolution":{"observed_at":"2026-08-14T05:27:18.172144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1702.06488","last_updated":"2018-01-10T14:53:30Z","snapshot_observed_at":"2026-08-14T21:15:28.355903Z","submitted_at":"2017-02-21T17:38:26Z","title":"Distributed Estimation of Principal Eigenspaces","version":4},"cited_work":{"arxiv_id":"1702.06488","doi":null,"metadata_source":"pith","pith_arxiv_id":"1702.06488","snapshot_observed_at":"2026-08-14T05:27:17.517212Z","title":"Distributed Estimation of Principal Eigenspaces","venue":"stat.CO","work_id":"6527bc33-97fc-4e79-ad46-79aba5e5a2c2","year":2017},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.301885Z"},"links":{"cited_paper":"/paper/1702.06488","citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:0cc7364b61acec4cbcfbcd9af9851558fee33768f4c52ca3aa34b54f387dddef","observation_id":"c130c69b-c2bf-4767-bf1e-89ff9c1227be","resolution":{"observed_at":"2026-08-14T05:27:17.525983Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.146517Z","title":"Discriminatory analysis-nonparametric discrimination: consistency properties,","venue":null,"work_id":"2b76f940-9cea-416e-bce2-86cf16470bc0","year":1951},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.307347Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:21b7e28fa719af846dd1dbec8e027070ed379f2f05fa2f4b8729718b79a30bf2","observation_id":"81a01d68-eda2-4432-b451-58e49ec52c4b","resolution":{"observed_at":"2026-08-14T05:27:18.152407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.129061Z","title":"Distribution-free exponential error bound for nearest neighbor pattern classiﬁ- cation,","venue":null,"work_id":"e2a8dbe9-1a00-42ac-a078-f18049aa8984","year":1975},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.312924Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:5b4bbbb5d5c9184c30dc0492c6e74aac5bc666989c478b3b6b0ff0418a3006b3","observation_id":"a719519d-2068-40c5-860a-29b98e6080d6","resolution":{"observed_at":"2026-08-14T05:27:18.134553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.110700Z","title":"Efﬁcient classiﬁcation for metric data,","venue":null,"work_id":"2c2e0d16-c867-4da4-91ad-b1867c53f9f6","year":2014},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.318171Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:ff894c3b9d1b22de8ab9fec75896c7e20a9b53c2c37ea3edd5a20fd913f549af","observation_id":"e5ffc2c8-25ce-4a43-8c70-c78e5b156b72","resolution":{"observed_at":"2026-08-14T05:27:18.116686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.093637Z","title":"Result analysis of the NIPS 2003 feature selection challenge,","venue":null,"work_id":"b6e24bde-de18-478e-abd3-68b66571fe88","year":2005},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.323016Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:32c726ae450622a6cd06a6253d6f04d83d1f8a619260e3ee77d76da3821e701e","observation_id":"ba90906b-6cb9-49c7-a2ab-5a6388b95df0","resolution":{"observed_at":"2026-08-14T05:27:18.099056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.074800Z","title":"The rate of convergence of k_n-NN regression estimates and classiﬁcation rules (Corresp.),","venue":null,"work_id":"e4d7fa2b-8bf9-401a-aebc-18d2f140b4d5","year":1981},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.328080Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:f96cede915cfc384e865ab0091f441103b3df87084106843971697fa333f9116","observation_id":"a4e0cd6d-2c03-4a5e-b030-ee7e70a4fe81","resolution":{"observed_at":"2026-08-14T05:27:18.080615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.058003Z","title":"Choice of neighbor order in nearest-neighbor classiﬁcation,","venue":null,"work_id":"c60fe244-aa59-41cd-9735-6680602f3c32","year":2008},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.333603Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:8d00c2a2d13fc3087feb96f08d7aee2259d5ce50723f1fdb95ad224b689fb037","observation_id":"f19dd598-4148-4435-ac91-b8c04fdb177d","resolution":{"observed_at":"2026-08-14T05:27:18.063802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.039391Z","title":"Properties of bagged nearest neighbour classiﬁers,","venue":null,"work_id":"20670008-7fc7-46d4-992c-bb198e83acf2","year":2005},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.338358Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:5a4d44cf7e4bf570f30ff98d1591dea080637588f068fec5472d63da23c7b696","observation_id":"70c2ef37-5352-4956-bdaa-c08e038efedd","resolution":{"observed_at":"2026-08-14T05:27:18.045672Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:18.019017Z","title":"Approximate nearest neighbors: towards removing the curse of dimensionality,","venue":null,"work_id":"d342c001-6ca7-463e-83fe-3ac3305d3941","year":1998},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.343050Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:b3ee2000681d3fe3cfcdd7b58a082d75dbcb107a5d6245e5dcf4344d6dbb5ead","observation_id":"b84b5472-bccf-4dc8-87d0-089addbe1f4c","resolution":{"observed_at":"2026-08-14T05:27:18.026905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.997168Z","title":"On the rate of convergence of local averaging plug-in classiﬁcation rules under a margin condition,","venue":null,"work_id":"2d535e0b-857b-4bc7-9518-e6dde4a1140f","year":2007},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.348137Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:1e2451c735274b4e305e1bb474e32818e6f57aa2e4b9b9e4ec931cdd245b3da4","observation_id":"3e897182-93b5-4715-b7d2-d8bcc6f44161","resolution":{"observed_at":"2026-08-14T05:27:18.002992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.979391Z","title":"Nearest-neighbor sample compression: Efﬁciency, consistency, inﬁnite dimensions,","venue":null,"work_id":"06e69b9d-39ce-4aa0-841e-e9b96c43b051","year":2017},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.353219Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:beab8f287c06850fd5538294dc374ca0011757569cf65b530372c9666e9f9086","observation_id":"8aa2aa4b-3a48-43d8-87fe-e55da5264301","resolution":{"observed_at":"2026-08-14T05:27:17.985479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.961968Z","title":"A Bayes consistent 1-NN classiﬁer,","venue":null,"work_id":"92d17633-57c8-4186-b669-0116e5089282","year":2015},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.358696Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:d8962ef4bd5e8a3f9a876ad3f6220ce3b1e0d6f34f3b0d16bd1a9fe8170e46e3","observation_id":"91d68198-90c7-4d72-8404-5cb39f7542f7","resolution":{"observed_at":"2026-08-14T05:27:17.966997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.943782Z","title":"Time-accuracy tradeoffs in kernel prediction: controlling prediction quality,","venue":null,"work_id":"e72e90eb-def0-49d6-9112-15b52234bb88","year":2017},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.364093Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:21f7170b3b59b1d8fe4506cbee1dae777a44c7ea5901fac6da77a7636b86194c","observation_id":"1482afd4-87db-40ea-b1bf-d20ca670cb6c","resolution":{"observed_at":"2026-08-14T05:27:17.950016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.923759Z","title":"Rates of convergence of nearest neighbor estimation under arbitrary sampling,","venue":null,"work_id":"dfa5b554-1c1d-480a-9098-bfa951662db9","year":1995},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.370779Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:4e4de9c72085751861059829c90a225babba0bb0f4f6262b79bc0d5794b6ef03","observation_id":"5629f549-53c0-4ad6-9ec6-d5971d7ea560","resolution":{"observed_at":"2026-08-14T05:27:17.929641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.902740Z","title":"Communication-efﬁcient Sparse Regres- sion,","venue":null,"work_id":"1390e2e1-1320-4c78-9000-bcae09118b11","year":2017},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.376846Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:afc99a1a4f889d249d89098b30c7bc8ff17acfaf5bfed1ebf8b34e4fe2be5557","observation_id":"b38d7ee9-6641-40d7-8565-ea0ef373efac","resolution":{"observed_at":"2026-08-14T05:27:17.910003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.877966Z","title":"Uci machine learning repository. university of california, irvine, school of information and computer sciences,","venue":null,"work_id":"45c7027a-41af-4e84-abf3-79f16ebff0d2","year":2013},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.382383Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:ae4fdbd981894d1fa8cd37abc2d644961b661866fe483f1c50c4713cb5952304","observation_id":"256a84b4-273a-43df-9dee-45ccc1e36aac","resolution":{"observed_at":"2026-08-14T05:27:17.885962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.859655Z","title":"Fifty years of pulsar candidate selection: from simple ﬁlters to a new principled real-time classiﬁcation approach,","venue":null,"work_id":"6a7994e7-a182-41c8-bf74-fdae83d79f59","year":2016},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.387772Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:f30459111148efcb18d956d54da275bf0666dee6a9c00c751ebf4d7d93b8c5f8","observation_id":"8abb6bea-ba1b-4a23-a002-ee6d1da20308","resolution":{"observed_at":"2026-08-14T05:27:17.865076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.840723Z","title":"Smooth discrimination analysis,","venue":null,"work_id":"287a601a-0cf0-4fa2-9a1e-02e9ee400129","year":1999},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.392882Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:8a17404bf94d9c835df68f87b440fd7726ceb97deebd5ae57b2fcdccf858fe63","observation_id":"3bab5bb7-cabb-43a1-bc68-5ad3c32c2fa0","resolution":{"observed_at":"2026-08-14T05:27:17.847654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.821332Z","title":"Scalable nearest neighbor algorithms for high dimensional data,","venue":null,"work_id":"922f1be0-9120-40eb-a2cb-009004335964","year":2014},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.398600Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:0003949fc862e6ae86ef41d2a969e172e631edeb8d31f3960aa079bd77342ce0","observation_id":"28ac0963-13eb-4f3c-b7e0-6b7d0c2770de","resolution":{"observed_at":"2026-08-14T05:27:17.827535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.803483Z","title":"Optimal weighted nearest neighbour classiﬁers,","venue":null,"work_id":"ab0946ec-a132-4147-8acf-c55f2d040c51","year":2012},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.406948Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:2f14c0ba180aac107bb19554fcc54bafa22dcfb983289a64123f1cdf8aab6644","observation_id":"d0178ac3-6c2d-49c5-9734-54fd8fcc138b","resolution":{"observed_at":"2026-08-14T05:27:17.809500Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.783733Z","title":"Computational limits of a distributed algorithm for smoothing spline,","venue":null,"work_id":"624da515-cf31-4d52-948b-a79c1377f7ab","year":2017},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.414324Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:f8981dcd002bb07dce6f236fee430540270b0b5751a7c424e84c048651ce29a0","observation_id":"994b581a-8da1-4638-af20-a82c2f3c87b2","resolution":{"observed_at":"2026-08-14T05:27:17.790269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.765736Z","title":"Locality-sensitive hashing for ﬁnding nearest neighbors [lecture notes],","venue":null,"work_id":"4c3d9c23-7366-4934-a450-93c9beeea1a2","year":2008},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.420403Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:ea3b61b37456411ca8e2c65f799e425f310221b174487a5080b9b23d2e5ad0dc","observation_id":"d21f2498-be71-4961-8ba8-d057727fb4e9","resolution":{"observed_at":"2026-08-14T05:27:17.771493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.745211Z","title":"Distribution inequalities for the binomial law,","venue":null,"work_id":"21bda09d-5bf9-408a-8188-24cb55608063","year":1977},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.425593Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:97763114556fbb80572819bc180f459286655afbd828db2c26dbbbf4c1c1d926","observation_id":"25381727-cdaf-4b12-a9d2-ce84b7b9ce9c","resolution":{"observed_at":"2026-08-14T05:27:17.751276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.722907Z","title":"Stabilized Nearest Neighbor Classiﬁer and its Statistical Properties,","venue":null,"work_id":"ed6c3444-661b-4181-a201-6000d624f10a","year":2016},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.430851Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:710d81e2345f2a8f10374d8ac5b7bdfbe4b1b889678183fc1e7ddf141c619d30","observation_id":"362d3b4f-442e-42be-a8b6-ba93ad74e260","resolution":{"observed_at":"2026-08-14T05:27:17.729821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.699689Z","title":"Optimal aggregation of classiﬁers in statistical learning,","venue":null,"work_id":"3c947265-860c-4a31-ba08-e3e12c844919","year":2004},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.436062Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:4cd1dc03ac25416267503bb393635ae70ac2444032da084dce2e4e5084d0b6c7","observation_id":"ed4fb20d-74c6-450d-b443-234a6ea77a56","resolution":{"observed_at":"2026-08-14T05:27:17.706208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.677738Z","title":"On the Uniform Convergence of Relative Fre- quencies of Events to Their Probabilities,","venue":null,"work_id":"3434b382-ff9e-4809-86d0-2dec553e2cc6","year":1971},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.440903Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:1c24156cca70c9dbaa08422ef7e74516d19a4fed384ec52640aedacf6d1befbf","observation_id":"a91b7a47-370d-429a-b7ff-8ddf9ba945ef","resolution":{"observed_at":"2026-08-14T05:27:17.683720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.659622Z","title":"Convergence of the nearest neighbor rule,","venue":null,"work_id":"6a04ca13-00ca-482b-9868-336e8edb04c3","year":1971},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.446624Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:088741eb8c6a70a9c16428bf448e633eb6e0a58f8cc630b2ac1f00193ebd59ff","observation_id":"10c207a3-d780-430f-90be-932424a2eb72","resolution":{"observed_at":"2026-08-14T05:27:17.665389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.640441Z","title":"Achieving the time of 1-NN, but the accuracy of k-NN,","venue":null,"work_id":"45e6ccaf-c35a-4966-99b1-732ffc88cd94","year":2018},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.452991Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:76c226a87d6e4e629ad7f39e1f1515f47d7bf16af04d12369a669d4e2d48d6b9","observation_id":"32c75bf3-4c4c-440d-8b34-0546f09e89da","resolution":{"observed_at":"2026-08-14T05:27:17.646588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.620783Z","title":"The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients,","venue":null,"work_id":"94124d38-cd37-41bd-9491-22941b5c59fe","year":2009},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.458207Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:956a55722704e6ff2d361f06f33c7c20aef04246f5c60fd6119036c746613407","observation_id":"1a338b9e-5f00-4058-9bd2-eb58566aa6dc","resolution":{"observed_at":"2026-08-14T05:27:17.627028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.589192Z","title":"Divide and conquer kernel ridge regression,","venue":null,"work_id":"5912b857-95e3-433e-8151-4ade2aa4b243","year":2013},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.463263Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:b56e46c2536fa50f446ffcda447c11ae2d00dfee8cabc4ee6e1d8c03beb5496c","observation_id":"8085b4b5-b62f-4578-af06-00c87c8334f6","resolution":{"observed_at":"2026-08-14T05:27:17.604948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T05:27:17.565107Z","title":"A partially linear framework for massive heteroge- neous data,","venue":null,"work_id":"050b4672-d0d6-4613-813a-0cc2223ace41","year":2016},"citing_paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T05:27:17.468595Z"},"links":{"citing_paper":"/paper/1909.01464"},"observation_digest":"sha256:44c1e035fa84b31bcecb0055b34374c2334a6400d5e1f6a652541a011b7d676b","observation_id":"9f8efb3e-2a6a-4a2a-9987-96257186bf9e","resolution":{"observed_at":"2026-08-14T05:27:17.572754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1909.01464","last_updated":"2019-10-31T02:10:29Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-14T05:14:44.080199Z","submitted_at":"2019-09-03T21:36:41Z","title":"Rates of Convergence for Large-scale Nearest Neighbor Classification"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":2,"verified_fuzzy":46},"total_outbound_references":48},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:1909.01464."}