{"as_of":"2026-08-18T15:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:57e6bb969b70ac727fd2f55a9a30404ee050ab91b76f3713cee7e3afbe673b3f","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:47:25.684755Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2507.02215/citation-record","integrity":"/paper/2507.02215/integrity","json":"/paper/2507.02215/citation-record.json","paper":"/paper/2507.02215"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:47:31.189488Z","title":"Adcock , Optimal sampling for least-squares approximation , Foundations of Computational Mathe- matics, (2025), pp","venue":null,"work_id":"8b8da487-8a50-4067-9215-7d7534349d3d","year":2025},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.031282Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:86f1d470eed371aa74a0306c8c0bfda8814ad114dda77734d4d5d75d4a736355","observation_id":"82ec56c1-2f14-4124-a9ef-8e2f6a1ad6d9","resolution":{"observed_at":"2026-08-06T20:47:31.192633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.00135","last_updated":"2018-08-30T22:27:06Z","snapshot_observed_at":"2026-08-18T06:08:57.140705Z","submitted_at":"2017-03-01T04:59:18Z","title":"Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations","version":3},"cited_work":{"arxiv_id":"1703.00135","doi":null,"metadata_source":"pith","pith_arxiv_id":"1703.00135","snapshot_observed_at":"2026-08-06T20:47:26.408664Z","title":"Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations","venue":"math.NA","work_id":"2c3dcb2b-d619-4d2f-9a93-87c2c647a79e","year":2017},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.145742Z"},"links":{"cited_paper":"/paper/1703.00135","citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:a77a4ee9d3ff19dd6c77129967683c67010a6683cc658fa736668f4386b29483","observation_id":"70bd36f8-d5c3-4995-9f23-49a8de9afbf7","resolution":{"observed_at":"2026-08-06T20:47:26.452898Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.180232Z","title":"Adcock, S","venue":null,"work_id":"85ff7569-2b08-492e-805e-8b94c390fde7","year":2022},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.294029Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:a49264c925ea1c81c1eb85758a1ed270cd93445dab6991d14e11ba8aaf4a721d","observation_id":"d6f60126-e55a-4107-ab30-1222fd42b021","resolution":{"observed_at":"2026-08-06T20:47:31.183104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.171768Z","title":"Adcock and J","venue":null,"work_id":"3c2a4c78-e5f4-4ba0-9cb7-044f19f45519","year":2020},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.438744Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:38c965df516798fc5b4d498d00ff0aa72d83da28b2d053b873539fddab763568","observation_id":"23363193-01a1-42ec-a6f2-93f343169094","resolution":{"observed_at":"2026-08-06T20:47:31.174402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.163158Z","title":"Alla and J","venue":null,"work_id":"7b94472e-f699-4adb-9cdc-fc24a9cf4e6d","year":2019},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.528943Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:227e4873a494dab246d29f479db270dba308aabbd6a0352303fc066c1f25d6f6","observation_id":"0e0a82f9-1a0c-4c89-92c9-3375ee530e26","resolution":{"observed_at":"2026-08-06T20:47:31.166477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.155420Z","title":"A vron, M","venue":null,"work_id":"09ca7420-f739-4749-86d8-30b2764ff6d2","year":2017},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.635549Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:044cbeb62e21f012ec303e81f8bf4fb923b2e6d63c093dc29b56c55f373721fd","observation_id":"69b018ed-a0da-4888-8150-f26fd2f3a6e4","resolution":{"observed_at":"2026-08-06T20:47:31.158117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.147246Z","title":"Bach, On the equivalence between kernel quadrature rules and random feature expansions , Journal of machine learning research, 18 (2017), pp","venue":null,"work_id":"4dffcda4-38ff-4812-9dce-f4bb9f00fa96","year":2017},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.692613Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:dc1620579f0cdf97997de65e46876df1fa60535109e808b1300013202b817b05","observation_id":"cb798b69-8285-465c-ba82-431bec988bdf","resolution":{"observed_at":"2026-08-06T20:47:31.150175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.138109Z","title":"Bendat and S","venue":null,"work_id":"2ecc1f6c-78c1-41d0-bcbb-6e92d4933452","year":1955},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.781376Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:158583450f5594652d0b23a7081b5cc60ba104dbc0bee0a23ddec39164651ca2","observation_id":"d5c4952c-0ac2-4578-b7d8-5f8e0d74364a","resolution":{"observed_at":"2026-08-06T20:47:31.141146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.126799Z","title":"Borwein and A","venue":null,"work_id":"61c2d2d9-5801-47fa-97c5-6fd8d16cee8d","year":2006},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.829137Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:b20b933bba06908dcfa950d5ac6940ef0a4c38f37b7dace7aca4478e4ab7de8c","observation_id":"3ba2f3ca-dded-4648-acc3-ccaa3cbb3c0b","resolution":{"observed_at":"2026-08-06T20:47:31.132498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.114065Z","title":"Cohen, M","venue":null,"work_id":"b3b36bd0-bddd-4b40-9c50-35bd2857d408","year":2013},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.904866Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:79e3e3104c307e878eb26779cd537e887e2a55a1e14b3b67c13ac42cf8f829aa","observation_id":"8cfc129d-dcc1-4f16-9a72-706db1ff4ec7","resolution":{"observed_at":"2026-08-06T20:47:31.118875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.099642Z","title":"Cohen and G","venue":null,"work_id":"0fbf9574-c579-4e60-943d-2e2351108470","year":2017},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:22.975868Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:d5c9b74bfa081535dbc844ce7ead5915981cf261c82491fcd42d115705a385b1","observation_id":"d3cdbba5-81ec-494f-8984-e42e4221241c","resolution":{"observed_at":"2026-08-06T20:47:31.106951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.075792Z","title":null,"venue":null,"work_id":"ad2f7cd4-4272-4b90-8dfe-811632bccd8d","year":2015},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.064631Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:72bb6944ab4a54eab00eb459dccb66bf0ba2a2759beb51bed8677312c65b085d","observation_id":"85df40ef-69e1-4b34-a192-bdc4ea4d8185","resolution":{"observed_at":"2026-08-06T20:47:31.083326Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.061838Z","title":null,"venue":null,"work_id":"c9f5f83e-5448-4f97-aa80-e7c98858c8cf","year":2015},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.154875Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:cfd31191a76a1ada243d0bafeaa6eef2aebb69c854f3ab29d750f47d4841c1e8","observation_id":"4f33676c-effd-4849-bf61-3260925ef173","resolution":{"observed_at":"2026-08-06T20:47:31.067230Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.032469Z","title":"Glasserman, Monte Carlo methods in financial engineering , vol","venue":null,"work_id":"3c842cd5-915f-4039-ac36-aa0883ca18c5","year":2004},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.253340Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:070e49676b5a02e8d5314b152d921de0e27f056ae0840fa89e7af917742e52ed","observation_id":"65cb789b-345e-4743-b182-8a855564f281","resolution":{"observed_at":"2026-08-06T20:47:31.035613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.023707Z","title":null,"venue":null,"work_id":"d0e035b6-ba70-40a2-af14-1bf3ddb2af32","year":2018},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.333043Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:bfaf65e66495de1a3c77c72009f0c841df1bd81d91d501da51b5ceb00d46f28f","observation_id":"0cf53c85-87a1-44bc-a597-a39ef1cb38cb","resolution":{"observed_at":"2026-08-06T20:47:31.026579Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:31.006654Z","title":null,"venue":null,"work_id":"0d9acd4d-fbce-4fb7-b765-0c2cb0dd66e9","year":2020},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.416329Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:c8698babbb8294e8ba6ad12e05ce5b2b5fb2a58360ead72554756573f4f2699f","observation_id":"b34e4edd-0e7d-4b4a-b99c-4fbb1af44927","resolution":{"observed_at":"2026-08-06T20:47:31.014735Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:30.996701Z","title":"Haberstich, A","venue":null,"work_id":"68a07397-35d1-4be2-a763-94c656bd7ef0","year":2022},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.483409Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:b351a84d56bb65fd0c52a3255bbb14a74a5161ea7e33e6b34f2c6197e554466c","observation_id":"073dc1d8-7614-4d41-b3cc-242503ccba6d","resolution":{"observed_at":"2026-08-06T20:47:30.999829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:30.984741Z","title":"Hadigol and A","venue":null,"work_id":"6b628da0-6e43-41f7-822a-0c17c3564dd8","year":2018},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.568109Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:a5a90e6170d6b69cb0ee090f91e64d5bdd50e559de68168e6e0de93459b7e15b","observation_id":"655da6ab-a8dd-476f-b720-8c5b9d1ba812","resolution":{"observed_at":"2026-08-06T20:47:30.990627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.08461","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:47:26.243041Z","title":"Herremans and B","venue":null,"work_id":"de9676f3-b316-4438-9094-27069e6c9499","year":2025},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.629938Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:13d193b6c0cccdc04e2890123610eb8d71b6ff36682ea0c4c7fb618c16486773","observation_id":"76928445-efd4-4be0-b8a1-82ae6e3a6b46","resolution":{"observed_at":"2026-08-06T20:47:26.287221Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.02347","last_updated":"2020-09-30T00:31:30Z","snapshot_observed_at":"2026-08-13T13:41:44.485229Z","submitted_at":"2020-05-05T17:32:37Z","title":"Differential Machine Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.02347","snapshot_observed_at":"2026-08-06T20:47:23.695680Z","title":"Huge and A","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.695680Z"},"links":{"cited_paper":"/paper/2005.02347","citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:a3d73268e838eb1c0cce9aef21764d5a044d88033d50757f6f023292c003670b","observation_id":"57243ccd-b28b-491d-9bff-58c964a64606","resolution":{"observed_at":"2026-08-06T20:47:23.695680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:47:30.855156Z","title":null,"venue":null,"work_id":"81ab672f-d5fa-46da-b555-b2b697ffa267","year":2020},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.762449Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:cdd681068bdedf282d3cb1a5002063949f107187b94034bc46dc90c5a82ae9bb","observation_id":"0c5a6394-27c0-4955-90c3-966b2c7ad0b1","resolution":{"observed_at":"2026-08-06T20:47:30.938359Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:30.549969Z","title":"Lewis, Finite dimensional subspaces of lp, Studia Mathematica, 63 (1978), pp","venue":null,"work_id":"15924bdf-9df0-4b4d-880e-c0b34e88bba1","year":1978},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.828186Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:4cead7d7200463cfbc8696a49fb9cc3e6ba36197213c13520b5ab71173c3c8ad","observation_id":"d915a0bd-4a7d-45dc-8025-1d88da71ef79","resolution":{"observed_at":"2026-08-06T20:47:30.664154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:30.237494Z","title":"Li, Compressed Sensing and Matrix Completion with Constant Proportion of Corruptions, Constructive Approximation, 37 (2012), pp","venue":null,"work_id":"f6ee5a87-77f5-41da-9683-a25e185967ad","year":2012},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.904511Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:1ba640d652e997814ebcf31ea7f602b8efaf821667f54876b1be98be20fb6884","observation_id":"01f68443-e92d-4911-8599-4cae4f25bdac","resolution":{"observed_at":"2026-08-06T20:47:30.353752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:30.029136Z","title":null,"venue":null,"work_id":"d16893c8-363a-4f65-ac7b-3364002e015d","year":2021},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:23.976551Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:38612db2363ba84f825da49a03c85ff6ab46371b27da313b95018946f5189145","observation_id":"3cc2786c-1486-4867-9302-0ecbfa75ee64","resolution":{"observed_at":"2026-08-06T20:47:30.136331Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.05662","last_updated":"2025-04-25T22:01:58Z","snapshot_observed_at":"2026-08-16T16:33:01.221316Z","submitted_at":"2022-09-13T00:35:54Z","title":"Fast algorithms for least square problems with Kronecker lower subsets","version":2},"cited_work":{"arxiv_id":"2209.05662","doi":null,"metadata_source":"pith","pith_arxiv_id":"2209.05662","snapshot_observed_at":"2026-08-06T20:47:25.951789Z","title":"Fast algorithms for least square problems with Kronecker lower subsets","venue":"math.NA","work_id":"fdc8b739-6259-4967-b0ee-e31301678c76","year":2022},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.039152Z"},"links":{"cited_paper":"/paper/2209.05662","citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:7cc1c5250f3a2a7b573a6e2351965bd77dbf447b82440cde61c835434a2ee1d8","observation_id":"3fac349f-f256-496e-a154-4e462215b9e6","resolution":{"observed_at":"2026-08-06T20:47:26.029636Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:29.740967Z","title":"Martinsson and J","venue":null,"work_id":"b1786b86-0f55-46d8-ad0e-19966c90de78","year":2020},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.112538Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:ec48468d5caa28cb57c9cc1314e56af637c6346c586550d55dd6d7bc57057ee0","observation_id":"dea3feb0-1fcb-4dfc-ac67-565a601b33ea","resolution":{"observed_at":"2026-08-06T20:47:29.855852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:29.556692Z","title":"Matsuda and Y","venue":null,"work_id":"0c7b4956-6e8e-4d8a-bf23-e9922593ac05","year":2025},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.177392Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:b5cbe7daaf6c52647e662515ec122e3a5529a5d7a51ac2ce98963095e576479a","observation_id":"fa25bead-e3c8-4ecc-a0a1-d9c4b4fffb13","resolution":{"observed_at":"2026-08-06T20:47:29.657484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.11474","last_updated":"2023-04-12T20:56:40Z","snapshot_observed_at":"2026-08-18T06:08:21.525179Z","submitted_at":"2023-02-22T16:21:37Z","title":"Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.11474","snapshot_observed_at":"2026-08-06T20:47:24.210682Z","title":"Murray, J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.210682Z"},"links":{"cited_paper":"/paper/2302.11474","citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:66d03e2398220c22de36e806efb8cd70babe85d72185da3883ce09072096a747","observation_id":"adcdb13f-b3c3-4b73-af8e-c939f52118d9","resolution":{"observed_at":"2026-08-06T20:47:24.210682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:47:29.483525Z","title":"Narayan, J","venue":null,"work_id":"b15b5b56-ca1e-44c1-bc0b-8e03464acee0","year":2017},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.285183Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:8b915e67478eb0f5a9a45983e6aff05abb008a69a4d0d6318a50fbba782c40c9","observation_id":"79f0f4ac-c5af-44d6-808c-9962fc0cb254","resolution":{"observed_at":"2026-08-06T20:47:29.551224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:29.258732Z","title":null,"venue":null,"work_id":"bd5499da-f373-486e-a268-dfabb0ffa23e","year":2021},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.370012Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:b30691aa81510b78638085f5fefab9068d1dc2516d0a3496d3348ff007889224","observation_id":"2f01b7ac-a473-482a-ad57-4ebc2f09aeab","resolution":{"observed_at":"2026-08-06T20:47:29.389731Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:29.125126Z","title":"Nevai, G´ eza freud, orthogonal polynomials and christoffel functions","venue":null,"work_id":"c2d45c17-0a4d-43ba-800f-feb9b7476c85","year":1986},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.451813Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:017c68b9f9e53ad49457aca393230f7129c825b4a79f3d22d084382c100b4a94","observation_id":"0b8d4122-37d1-4421-a59d-6cabf997022a","resolution":{"observed_at":"2026-08-06T20:47:29.192048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:28.976249Z","title":"Niederreiter, Random number generation and quasi-Monte Carlo methods , SIAM, 1992","venue":null,"work_id":"31b98514-9b62-417d-bd30-96b761d1a435","year":1992},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.511488Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:a5a440afb5c574f0b3dd720acd85f0bd71bcc59c8521282e5e9ca275aedb4a13","observation_id":"8706dfde-8b92-4716-98c2-d424bfa9f1be","resolution":{"observed_at":"2026-08-06T20:47:29.036425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:28.800971Z","title":"Olivares, A","venue":null,"work_id":"f096e061-105b-4e2d-a6bf-def183ef1808","year":2016},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.590512Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:140f3daed871832200e96cc1b62af1d56de8af44046df9e0eb13cf8e2c52a13f","observation_id":"1c3124f3-b62c-4360-9f82-a5e1a6a8a617","resolution":{"observed_at":"2026-08-06T20:47:28.896170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:28.598701Z","title":"Paszke, S","venue":null,"work_id":"f55a7cdf-80c2-44af-a610-5fc894ef6222","year":2017},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.702722Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:1dd9c739f36824cd5c48a71eaa884f39204923ae1153cdc4e591ec847b44097d","observation_id":"9eaa7ec3-5ff0-4b2a-9e4f-7adbb3035055","resolution":{"observed_at":"2026-08-06T20:47:28.699907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:28.459022Z","title":"Peherstorfer, Breaking the kolmogorov barrier with nonlinear model reduction, Notices of the Amer- ican Mathematical Society, 69 (2022), pp","venue":null,"work_id":"bd93a38f-99de-47d4-aed5-204c2ba5ce1e","year":2022},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.749923Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:1cb60b83d7e6dae5175d4ceee9cf8ad321ff6741772dc7038fdc8e29527dd937","observation_id":"d985d4e2-2b34-4289-948f-bf945f091fcd","resolution":{"observed_at":"2026-08-06T20:47:28.542410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.06682","last_updated":"2023-02-19T17:39:38Z","snapshot_observed_at":"2026-08-16T15:55:37.254121Z","submitted_at":"2023-02-13T20:44:16Z","title":"Parametric Differential Machine Learning for Pricing and Calibration","version":2},"cited_work":{"arxiv_id":"2302.06682","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.06682","snapshot_observed_at":"2026-08-06T20:47:25.780200Z","title":"Parametric Differential Machine Learning for Pricing and Calibration","venue":"q-fin.CP","work_id":"bca062e1-e9d3-4335-8aed-cac6c249e63b","year":2023},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.834641Z"},"links":{"cited_paper":"/paper/2302.06682","citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:7535e5cc4b40436fece3983ebd7571f6ee73554bf3114d0795c7ba07c8b21c14","observation_id":"4742fe77-9bfb-4df7-8c9e-1d8e5fb9e603","resolution":{"observed_at":"2026-08-06T20:47:25.855530Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:28.291386Z","title":"Pukelsheim, Optimal design of experiments , SIAM, 2006","venue":null,"work_id":"4cfe486a-d04c-4cbc-b658-fa1342eb89cc","year":2006},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.893415Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:26b02f0985d864d855600c407f143f0a34206cbc88e919e15c891fab446bf43e","observation_id":"332d7632-ce1b-4e65-9060-f08f9791eff8","resolution":{"observed_at":"2026-08-06T20:47:28.396593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:28.088066Z","title":"Rahimi and B","venue":null,"work_id":"4eb942b9-de08-4434-b6a1-89a80c877e43","year":2008},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:24.980426Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:ed9c84cfe1d317bb00d6a1a95bcbc8bdd559edf3053af451427287bcb9542893","observation_id":"e8150a20-82c3-4f88-96e8-92332919e34e","resolution":{"observed_at":"2026-08-06T20:47:28.157452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:27.915756Z","title":"Reiss and M","venue":null,"work_id":"2e68d5b6-aff9-4af3-8c73-05db1dccc376","year":2020},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:25.058760Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:6c80b9d1fde9d03edab7166d9fb491a9aaeff97dc50231ba729316908863ceab","observation_id":"32091aa6-69e3-49a1-ad92-2eec13072d35","resolution":{"observed_at":"2026-08-06T20:47:28.001677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:27.678415Z","title":"Shin and D","venue":null,"work_id":"2611f510-46de-43ac-9de7-98fcaf2ab38b","year":2016},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:25.147219Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:d3c37ae23c31fc072c577c508c711fb4f4de93aa87e7677a1a06bac07b7a625e","observation_id":"be642432-e8dc-47a4-9200-2e1c166c7188","resolution":{"observed_at":"2026-08-06T20:47:27.832535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:27.490481Z","title":null,"venue":null,"work_id":"0f6f8f30-633b-4d77-aeae-202e072c816a","year":2012},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:25.201310Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:5e44ecc8d989398540d8456bf24701640c906e8c3d0effb8120e836f450531ec","observation_id":"93fff7fa-3282-462b-a25a-924b0a3c5504","resolution":{"observed_at":"2026-08-06T20:47:27.550532Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:27.335145Z","title":"V apnik, Principles of risk minimization for learning theory , Advances in Neural Information Process- ing Systems, 4 (1991)","venue":null,"work_id":"987b0006-5592-448c-85b7-505758bc9a4e","year":1991},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:25.282127Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:bd08afef2992a7d153f747feb5e0f5c5b8be87ed2ab0915b47b150d552f76abd","observation_id":"57b4b66f-ebbb-4912-b6e3-3081b88a1e4c","resolution":{"observed_at":"2026-08-06T20:47:27.407351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:27.154841Z","title":null,"venue":null,"work_id":"b895397b-ee85-408f-94d1-d5e834be657b","year":2006},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:25.331308Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:9a17b6c3aab10ee582bc9455fb9d5348b5b96b1631b8e36276f6ddb045b73d0c","observation_id":"520b23fb-2db1-41f5-b3ec-e1ea1c006aef","resolution":{"observed_at":"2026-08-06T20:47:27.246217Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:26.919829Z","title":null,"venue":null,"work_id":"c03e3de4-f8ef-49d4-9b54-4cf7ef7e2e1b","year":2014},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:25.399818Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:651e40ed01d65fbc5294f1665b5645551b2c8d469c4eb53a2bbf6c53a81abac1","observation_id":"ce95f50b-918f-45d7-b157-5338d2ce93f8","resolution":{"observed_at":"2026-08-06T20:47:27.015246Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:47:25.525629Z","title":"Xiu, Numerical methods for stochastic computations: a spectral method approach, Princeton University Press, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:25.525629Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:210b83637ab6100731ca5ce04a6b896d1d22d7224f82de01b77a5aa7ea55689f","observation_id":"609e9df9-9df2-4165-a0ae-e90a6fa0535b","resolution":{"observed_at":"2026-08-06T20:47:25.525629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:47:26.767798Z","title":"Xu and A","venue":null,"work_id":"bd9d7276-777c-4224-90ec-c72951abb292","year":2023},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:25.584738Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:a729f8926a14c1747972b2e4a414ed15319b868e8277d0bb12d1506ec933af3f","observation_id":"b6382842-efc3-484f-8410-e1ffd227f2c7","resolution":{"observed_at":"2026-08-06T20:47:26.838269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-06T20:47:26.595346Z","title":null,"venue":null,"work_id":"eab2c4a4-6255-4723-aa29-6a5a27f12cec","year":2020},"citing_paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T20:47:25.684755Z"},"links":{"citing_paper":"/paper/2507.02215"},"observation_digest":"sha256:01b5f2f15f8df69e752293a31a8e5d05aecf680deba338082e26821ee6a45dcc","observation_id":"c9f2f5f9-4f11-4aaa-b631-9b31a1fe3aba","resolution":{"observed_at":"2026-08-06T20:47:26.686761Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.02215","last_updated":"2026-05-25T01:57:42Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-09T23:31:24.916432Z","submitted_at":"2025-07-03T00:31:29Z","title":"Hybrid least squares for learning functions from highly noisy data"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":4,"verified_fuzzy":29},"total_outbound_references":47},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2507.02215."}