{"as_of":"2026-08-14T00:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2868b7ac2f63e23f7fac74132027964898f3aa3946cddc20c49d1e7caf7e2a54","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:02:37.864567Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2608.09623/citation-record","integrity":"/paper/2608.09623/integrity","json":"/paper/2608.09623/citation-record.json","paper":"/paper/2608.09623"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:02:37.766468Z","title":"Elsevier, 2003","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.766468Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:9ac81854ac411502fbe0bc840bc96bead2614f878a734742648a694b3741b9c1","observation_id":"034260b3-f44f-4102-9c9f-831287d6216e","resolution":{"observed_at":"2026-08-11T14:02:37.766468Z","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":"2026-08-11T14:02:38.326995Z","title":"Bach, Gert R","venue":null,"work_id":"3ae46a66-4a59-430f-aa08-7d41cb262492","year":2004},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.770754Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:c39dd0a8a3481c2c4638b65491106f83768d88f56d92db1a6cfdba40d6bddde8","observation_id":"de80ba8c-983b-4c8e-bd54-5aa8ee162c02","resolution":{"observed_at":"2026-08-11T14:02:38.331679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.314157Z","title":"Academic press, 1988","venue":null,"work_id":"9ea2c33b-a2ed-475f-84d8-33765c089843","year":1988},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.775311Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:3c56025d35a558688fe0833c471d73576de58617470832e0e6feb6170bf9f865","observation_id":"3f8eb463-a988-4814-875e-d852bc8bdfbe","resolution":{"observed_at":"2026-08-11T14:02:38.318454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.299981Z","title":"Lipschitz regularity of graph Laplacians on random data clouds.SIAM Journal on Mathematical Analysis, 54(1):1169–1222, 2022","venue":null,"work_id":"d24215ec-e67c-4ba3-912b-a5ade9d15f18","year":2022},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.779662Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:d9b389f5e4555c9ab26c923016b444f952e5fb218570a10d3e6e1b45e0a90114","observation_id":"29f41dd6-9b0a-4ec9-b14a-0a90022c25ed","resolution":{"observed_at":"2026-08-11T14:02:38.304091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.285734Z","title":"Improved spectral convergence rates for graph laplacians onε-graphs and k-nn graphs.Applied and Computational Harmonic Analysis, 60:123–175, 2022","venue":null,"work_id":"64287810-599e-4ce5-8048-49f4b7ed3f99","year":2022},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.784021Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:9ee4d7a9e9fc7c05927cc69418232c2d928fd6893f5195277c20eabed7373143","observation_id":"b0e296c0-f64e-4a53-a6ae-9716986c075d","resolution":{"observed_at":"2026-08-11T14:02:38.290200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.273470Z","title":"Springer, 2008","venue":null,"work_id":"ae0956f9-0479-4dd6-b7b9-7f8e753a2823","year":2008},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.788231Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:4c044a307440897ae66f07c9c6b47d1fef5ad2180a496c7cfc9e5f5c629dca33","observation_id":"5dc223fa-1e8d-4361-965c-d10abed99014","resolution":{"observed_at":"2026-08-11T14:02:38.277156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:37.792798Z","title":"Diffusion maps.Applied and computational harmonic analysis, 21(1):5–30, 2006","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.792798Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:939beaade8287c09023344fed7f856044414043949b9f9e9b2bad9859cba9c60","observation_id":"0bf450e8-9074-4fba-802a-f171618c4dfe","resolution":{"observed_at":"2026-08-11T14:02:37.792798Z","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":"2026-08-11T14:02:38.250208Z","title":"Eigenvalues of the laplacian on a compact manifold with density.Communications in Analysis and Geometry, 23(3):639–670, 2015","venue":null,"work_id":"856a2003-e49e-4d5a-8f4d-37f33018e0a7","year":2015},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.796797Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:9bd925bc042b42f769daa0b6bfaf5184efcd966e04e8190c1a2492901b18db0b","observation_id":"3f7cfd64-698f-478c-843d-1a1b84df0e46","resolution":{"observed_at":"2026-08-11T14:02:38.254623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.235992Z","title":"Springer, 2019","venue":null,"work_id":"4132c921-4678-473c-9a57-dc536c3e5a5b","year":2019},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.800828Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:0f0c86f1f58a8d518bd4e11246205f75ebd25a160e23f5562a69b94a23dc8a09","observation_id":"2d5b091b-8743-4359-b124-40a76f6a1a10","resolution":{"observed_at":"2026-08-11T14:02:38.240105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.222118Z","title":"Elsevier, 2005","venue":null,"work_id":"f8bdfaaa-c67f-4726-9137-20202d36dd6a","year":2005},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.804742Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:fde63e5e210b1313a2e19ef95cab9000fb182bf4be6d7d86f79efbf41dd9179c","observation_id":"2bd1377d-f74a-4eb3-88c9-8e579ef2bd39","resolution":{"observed_at":"2026-08-11T14:02:38.226478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.208647Z","title":"Spectral convergence of graph Laplacian and heat kernel reconstruction in L∞ from random samples.Applied and Computational Harmonic Analysis, 55:282–336, 2021","venue":null,"work_id":"11e18c9d-bb00-480a-b0b8-d91ce0e268b2","year":2021},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.809444Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:895d0ecb78be1edd825b805193400a530a435c984b99f1fe9e464587694863ce","observation_id":"db0ac0d2-b63e-490f-9411-7692609d41e2","resolution":{"observed_at":"2026-08-11T14:02:38.213127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:37.813606Z","title":"Scattered data interpolation on embedded submanifolds with restricted positive definite kernels: Sobolev error estimates.SIAM Journal on Numerical Analysis, 50(3):1753–1776, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.813606Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:dc668bcb6484abfc80ef831ff0cffe090decd908d28be19fb2736630cd7dcc84","observation_id":"fa2cf8fe-6136-4f14-bbf6-73b25324c246","resolution":{"observed_at":"2026-08-11T14:02:37.813606Z","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":"2026-08-11T14:02:38.186188Z","title":"Rates of strong uniform consistency for multivariate kernel density estimators","venue":null,"work_id":"9739f10b-508b-4827-bc53-7385f2884a1b","year":2002},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.817677Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:4017431c8d87e7175d7411164ec38ef20c910d94da1f4e2592809666909ebe22","observation_id":"872ee20b-987b-4d48-9ed8-88c0f6d456a8","resolution":{"observed_at":"2026-08-11T14:02:38.190768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.173221Z","title":"Cambridge University Press, 2016","venue":null,"work_id":"671b1616-1e42-4ebe-aac6-b3a5db8d2ea2","year":2016},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.821688Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:2f3212c14f0ed57e044b44c721f948a6311bb72155ef2849c64ef649995e558a","observation_id":"ce89b1c3-13d3-43b4-9558-1add3b601aeb","resolution":{"observed_at":"2026-08-11T14:02:38.177292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:37.825732Z","title":"Radial basis approximation of tensor fields on manifolds: from operator estimation to manifold learning.Journal of Machine Learning Research, 24(345):1–85, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.825732Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:1b6eb6c6d2a40f95651c43d84a802ec7754b9711be5a484f2de0a16f07856231","observation_id":"716b8565-74a9-429f-9b5b-39a79f117b67","resolution":{"observed_at":"2026-08-11T14:02:37.825732Z","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":"2026-08-11T14:02:38.152080Z","title":"Ghost point diffusion maps for solving elliptic pdes on manifolds with classical boundary conditions.Communications on Pure and Applied Mathematics, 76(2):337–405, 2023","venue":null,"work_id":"49265015-3b87-42fc-8efe-270243afabd3","year":2023},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.829724Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:c373fe90c0e46b5e092073db6351d9cf00a64d755ad59a08d68ee19d467df9ac","observation_id":"51f50dbe-24a4-4c1f-a2fb-2eae9bbea7ec","resolution":{"observed_at":"2026-08-11T14:02:38.156100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2607.00257","last_updated":"2026-06-30T23:07:55Z","snapshot_observed_at":"2026-08-11T18:28:00.481792Z","submitted_at":"2026-06-30T23:07:55Z","title":"Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression","version":1},"cited_work":{"arxiv_id":"2607.00257","doi":null,"metadata_source":"pith","pith_arxiv_id":"2607.00257","snapshot_observed_at":"2026-08-11T14:02:38.039591Z","title":"Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression","venue":"cs.LG","work_id":"c5eb32d9-a9e2-45bc-b287-298f5ed31c0b","year":2026},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.833720Z"},"links":{"cited_paper":"/paper/2607.00257","citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:64e64139ff4f6547cddd628fbc8d9c92630988e12ae2a1cda781d16ed27a7e57","observation_id":"b70d4df7-120a-425e-ba5a-764b460a7f76","resolution":{"observed_at":"2026-08-11T14:02:38.046096Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.139868Z","title":null,"venue":null,"work_id":"0231de26-c31b-4c13-9977-6ff5cc0bb558","year":2004},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.838175Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:224bae6354dea3d69202f5e325d262df1b10f45bf11633410bad5c4d1569524d","observation_id":"0409aad4-216c-430f-b91f-6ab709ce0113","resolution":{"observed_at":"2026-08-11T14:02:38.143794Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.127270Z","title":"Kernel flows: From learning kernels from data into the abyss.Journal of Computational Physics, 389:22–47, 2019","venue":null,"work_id":"368073bc-d347-45e5-9461-cad74478f6f0","year":2019},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.842039Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:b8f98289d41b64607236b8243e9355e5f4798d01b8251ab0ae349381531da520","observation_id":"2ead571f-9c4f-4f4a-8618-792fb2f4bb33","resolution":{"observed_at":"2026-08-11T14:02:38.131654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.111849Z","title":"Wilson Peoples and John Harlim","venue":null,"work_id":"a1c958f3-6639-4405-b2bd-8544481b04a0","year":2026},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.846149Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:422c521321bd010e26ae3ec65e5c1df066dd385e1ab68e03caa62f868498612e","observation_id":"f486edb7-6964-4b3e-845a-33c7df35c4d7","resolution":{"observed_at":"2026-08-11T14:02:38.117009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.097247Z","title":"Smola.Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","venue":null,"work_id":"3e5ed677-003f-41fe-acba-e77a9d5703b2","year":2002},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.850386Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:a0b0e27a70c560ac930a4ec40c13047791e72d042f63fb1594da8a8bd9486ba2","observation_id":"4ac5cd16-1c7c-4431-9644-db4dea311e2e","resolution":{"observed_at":"2026-08-11T14:02:38.102295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:37.854347Z","title":"Learning solution operator of dynamical systems with diffusion maps kernel ridge regression.arXiv preprint arXiv:2512.17203, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.854347Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:b5e45d5fa410eddfd2ad9430dbdc60a84220079d7dae9418cb966bc508cca0d0","observation_id":"e38f5d16-69e5-4deb-8911-ab36897ece75","resolution":{"observed_at":"2026-08-11T14:02:37.854347Z","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":"2026-08-11T14:02:38.083585Z","title":"Optimal rates for regularized least squares regression","venue":null,"work_id":"eee0b667-a881-438e-8687-9ab90bcab421","year":2009},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.857767Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:53dcec1cbe329346035234c13c71f47f3135dce9f4a0c20c2bc5a4261e7e300d","observation_id":"441d7025-a1f0-482b-ac73-2b1e53beb08b","resolution":{"observed_at":"2026-08-11T14:02:38.088021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.069555Z","title":"Consistency of spectral clustering.The Annals of Statistics, pages 555–586, 2008","venue":null,"work_id":"576118fe-b88a-413f-9cc5-6b2aa98e3880","year":2008},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.861153Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:8676ec75d41009ad3b0b88732a5a9f3b25362ca29d4a0c69352b636d3a7cd667","observation_id":"76632093-39cd-465a-9faf-af8e8f7f995f","resolution":{"observed_at":"2026-08-11T14:02:38.073966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T14:02:38.056097Z","title":"f(x) m0q(x) +ϵ Ã 1 m0q(x) m2 m0 µ ∆g f(x)− f(x)∆ g q(x) q(x) ¶ −f(x) m2 m2 0 µ ω(x) q(x) + ∆g q(x) q(x)2 ¶! +O(ϵ 2) # . Subsequently, ˆqϵ(x)= eGϵ1(x) qϵ(x) =ϵ −d/2","venue":null,"work_id":"f3215cca-35b7-404b-89dd-b63979d2db87","year":2004},"citing_paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T14:02:37.864567Z"},"links":{"citing_paper":"/paper/2608.09623"},"observation_digest":"sha256:439e6278205bb31cf7d68ee14171b151d89ede9760699998c94fe2b57dcd594d","observation_id":"1145b5dd-3fb8-4f2d-acf4-6addb0102728","resolution":{"observed_at":"2026-08-11T14:02:38.060387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.09623","last_updated":"2026-08-10T14:05:30Z","latest_version":1,"primary_category":"math.NA","snapshot_observed_at":"2026-08-13T23:45:39.959286Z","submitted_at":"2026-08-10T14:05:30Z","title":"Diffusion Maps Kernel Ridge Regression"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":18},"total_outbound_references":25},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2608.09623."}