{"as_of":"2026-08-15T14:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:65911d1f6b96b8681cec4d3b4846a94131933332048382da88105da4853e0698","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T10:18:34.186208Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2607.20309/citation-record","integrity":"/paper/2607.20309/integrity","json":"/paper/2607.20309/citation-record.json","paper":"/paper/2607.20309"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.020372Z","title":"Minimax optimality of deep neural networks on dependent data via pac-bayes bounds","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.020372Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:66263015d45560ce80a79150197a7a2f4dad0f0f09fda0360101f08f34aea13d","observation_id":"e1b941ff-f3a5-4108-819c-6f8f62eca55d","resolution":{"observed_at":"2026-08-01T10:18:29.020372Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.171149Z","title":"On deep learning as a remedy for the curse of dimensionality in nonparametric regression","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.171149Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:62f0477dda424ad1671e46bfdf10e246792f531063ff24822dd365917c38c8bd","observation_id":"50a27605-8f7d-450c-a66c-70318120aaa3","resolution":{"observed_at":"2026-08-01T10:18:29.171149Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.239655Z","title":"l1-penalized quantile regression in high-dimensional sparse models","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.239655Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:81a22c9d856b8cc8e3d56dc8a43891b4f581638d61f990bf693820603ba339b7","observation_id":"9f0d84ab-ac95-46fa-99da-7af0627f7812","resolution":{"observed_at":"2026-08-01T10:18:29.239655Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.310328Z","title":"Geometric ergodicity of nonlinear autoregressive models with changing conditional variances","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.310328Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:25fe55dc3474754437962ee3b53f9bef31f88ba9400892ff2081909208fab899","observation_id":"0613aba3-3ca0-4709-b6df-10260dceb207","resolution":{"observed_at":"2026-08-01T10:18:29.310328Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.392849Z","title":"Learning bounds for importance weighting","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.392849Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:e7a7326382a7aed7fc6b2e6f12be800db5a55b0f45b256eb669e35dacaf2faa2","observation_id":"457246a8-dde2-4cd7-bb06-da1396d2fcac","resolution":{"observed_at":"2026-08-01T10:18:29.392849Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.474509Z","title":"Variable selection and estimation with the seamless-l 0 penalty","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.474509Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:a51678e42137ceee0e6b5b717685805ad82c3a0626af957ac0c396472db16e8d","observation_id":"a7e63052-eb87-4e4e-8a2b-61e20be53fc2","resolution":{"observed_at":"2026-08-01T10:18:29.474509Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.550860Z","title":null,"venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.550860Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:9155326c15bdbb9cb90d473904e3871d38237896b2818183100d90dedf2f564a","observation_id":"3d3f6243-2a9b-4482-923d-fbf48a1690de","resolution":{"observed_at":"2026-08-01T10:18:29.550860Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.632716Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.632716Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:6d9706372ae4400c67a8b48a0d1f316f0037830162ec5b2e11cd9909cb7cc07c","observation_id":"7aabb2cb-b2e6-4e58-8efa-73fe67dd313d","resolution":{"observed_at":"2026-08-01T10:18:29.632716Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.688433Z","title":"How do noise tails impact on deep relu networks? The Annals of Statistics 52 , 4 (2024), 1845--1871","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.688433Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:522b82951e8a5dd496d03d8e4f41e6b3b683c6bd706524caab1b6596ff7e1a51","observation_id":"0ee94623-1b5c-49dd-8235-306ec8f8571a","resolution":{"observed_at":"2026-08-01T10:18:29.688433Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.763636Z","title":"Variable selection via nonconcave penalized likelihood and its oracle properties","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.763636Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:b81370ffa2017a65b5878dea6e1146d5b1e0cfda584b0b021d02b2f5236a66e2","observation_id":"e21ace87-e304-40f5-b5eb-c07157889d72","resolution":{"observed_at":"2026-08-01T10:18:29.763636Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.845641Z","title":"Deep nonparametric quantile regression under covariate shift","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.845641Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:6c5d3741dd3a448be0107c6843058de32bb29b0a684d290c9bd1a34ee6f0f6e0","observation_id":"56db4b33-4fc6-4123-919f-f9e71ac9641a","resolution":{"observed_at":"2026-08-01T10:18:29.845641Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:29.926003Z","title":"Towards a unified analysis of kernel-based methods under covariate shift","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:29.926003Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:bfd05692ad3b86096aba1f4d2901eafc35f03a7b642f112507a1b5bfa288d042","observation_id":"effab523-0651-454f-93ad-14b98c016d9e","resolution":{"observed_at":"2026-08-01T10:18:29.926003Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.003881Z","title":"Domain adaptation for medical image analysis: a survey","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.003881Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:57d718e92fe17017670f870b6ef146b6a223a54f606ad5e331fd2165a6037d83","observation_id":"ad274789-5d0f-4ad8-b64a-ec16dfefaf82","resolution":{"observed_at":"2026-08-01T10:18:30.003881Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.062671Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.062671Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:0ac5030275044086163507d4ef9eb51470faf88abf04f9b3e77a5df4a2d88c98","observation_id":"6b8d9ebb-8a12-43d1-a471-ed1586ffa469","resolution":{"observed_at":"2026-08-01T10:18:30.062671Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.134468Z","title":"A bernstein-type inequality for some mixing processes and dynamical systems with an application to learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.134468Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:412eba7b063b8fc99a0da4c97741998ec7e3e82a5b8ab0284e14410484d1fd1f","observation_id":"e03448c1-1296-4c85-aeb6-0bff74034444","resolution":{"observed_at":"2026-08-01T10:18:30.134468Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.190192Z","title":"Deep neural networks learn non-smooth functions effectively","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.190192Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:7c6764a4c07e98222a601338b75f386fedfee6711cf562ca807a6101f047fd35","observation_id":"c424e52a-eb02-42a4-bfde-d1417e94f1e8","resolution":{"observed_at":"2026-08-01T10:18:30.190192Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.246237Z","title":"Advantage of deep neural networks for estimating functions with singularity on hypersurfaces","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.246237Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:ff6b5cfb7f6109e2e69c5984bbc1f183c10188650af66e0e41307c28c7237304","observation_id":"a201de05-878c-4f14-ab4a-27b5f215f7ea","resolution":{"observed_at":"2026-08-01T10:18:30.246237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09383","last_updated":"2025-01-05T03:45:00Z","snapshot_observed_at":"2026-08-12T22:24:55.608325Z","submitted_at":"2024-10-12T06:24:35Z","title":"Deep Transfer Learning: Model Framework and Error Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09383","snapshot_observed_at":"2026-08-01T10:18:30.378082Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.378082Z"},"links":{"cited_paper":"/paper/2410.09383","citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:5969cee5e72b06fb62a3fba041395d9de6a9ea034a9a5a39a55d014dbdc38557","observation_id":"6aa420ca-5d52-40ef-829e-b0ed73f19de5","resolution":{"observed_at":"2026-08-01T10:18:30.378082Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.486318Z","title":"Deep nonparametric regression on approximate manifolds: Nonasymptotic error bounds with polynomial prefactors","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.486318Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:d2558af5ac18d228643bc38bb0f07d562f2fdd34207442304bb8d837f62e27b8","observation_id":"3bf71ee3-8180-4f2a-947c-5e7bc8582da9","resolution":{"observed_at":"2026-08-01T10:18:30.486318Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.598690Z","title":"Excess risk bound for deep learning under weak dependence","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.598690Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:f14c4afc19044119713f89d184412fe93bf86f80771fccd840e270af47f085da","observation_id":"da71cbe0-e058-4f0a-ae77-5c6546aab021","resolution":{"observed_at":"2026-08-01T10:18:30.598690Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.706482Z","title":"Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.706482Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:8176b93025ce37ed61ce2841901fdd8ac6cbd2259da14aa42d87152a12f5fc7f","observation_id":"12209bbf-6eb6-4024-8e8a-7a7fc788bc10","resolution":{"observed_at":"2026-08-01T10:18:30.706482Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.792488Z","title":"A general framework for deep learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.792488Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:ed5fda98eaa24ab6bec00edfde998b8a08ed8b1cd9f1c70f067d784ba65dc3d1","observation_id":"4baaffff-6753-48d9-bc74-ada048b67fcc","resolution":{"observed_at":"2026-08-01T10:18:30.792488Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:30.951598Z","title":"Robust deep learning from weakly dependent data","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:30.951598Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:d9e61dbb42eb8d17ea18a74e0edf01acd2b1859f3f95937b8ea849ecc3f49ceb","observation_id":"4a97ac6e-db26-41aa-b088-e1a2da7ce63a","resolution":{"observed_at":"2026-08-01T10:18:30.951598Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:31.089195Z","title":"Fast convergence rates of deep neural networks for classification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:31.089195Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:e7f00895779b9d0b7ca75bb392b448744979352962438217c3c6956e58910d81","observation_id":"74edd4a3-1283-4d15-bcf6-516a2d38658b","resolution":{"observed_at":"2026-08-01T10:18:31.089195Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:31.183554Z","title":"W., Sagawa, S., Marklund, H., Xie, S","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:31.183554Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:39abdc81bb75d36f7a8673e612399d132dfceb75e5ffb201ebc1aaa7fef711e9","observation_id":"01587f54-aa43-4005-8117-610524845be3","resolution":{"observed_at":"2026-08-01T10:18:31.183554Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:31.352657Z","title":"On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:31.352657Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:df5ebd94348ef2ac69843c82e7768ea135e3a7f907a7bd2f8cdd1a9b74b7e553","observation_id":"8d28374c-698e-41da-a68c-1bb707a9e5cd","resolution":{"observed_at":"2026-08-01T10:18:31.352657Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:31.487351Z","title":"Adaptive deep learning for nonlinear time series models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:31.487351Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:17cf31376ff5cb7e28407efb7f91e50b351368bcca8198fc1fe1afd21ddd8793","observation_id":"19b7413d-791b-4bd9-b46e-a01cc8b04149","resolution":{"observed_at":"2026-08-01T10:18:31.487351Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:31.606675Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:31.606675Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:a961ecb7859581f445b99b47c567831640cfdf9a90eb05fd3b1a3ba4857855a4","observation_id":"edc790df-41f0-4043-bbae-0b779a747087","resolution":{"observed_at":"2026-08-01T10:18:31.606675Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:31.801606Z","title":"Theoretical analysis of deep neural networks for temporally dependent observations","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:31.801606Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:6d7bea0e3b44e4712c04c5e4faa4d4a008a7a237de8b81d9f955dbe882387e06","observation_id":"507d0727-e9ae-43ea-b4b7-b745d8b1a911","resolution":{"observed_at":"2026-08-01T10:18:31.801606Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:31.966346Z","title":"H., and Chatterjee, S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:31.966346Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:af6b164f5317058b70efc375b9dd4f026eea8c0b5ab7e93cd3c6394739570f30","observation_id":"6f39acf3-f6f4-4df4-8ea7-28822d7af4ed","resolution":{"observed_at":"2026-08-01T10:18:31.966346Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:32.057440Z","title":"Exponential inequalities and functional estimations for weak dependent data: applications to dynamical systems","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:32.057440Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:9c27fae33878546cb0c4202113245c631b1589e180fb2466e524d521a5faf5c8","observation_id":"3d42a108-feb3-4913-bdec-9377fca7ac1f","resolution":{"observed_at":"2026-08-01T10:18:32.057440Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:32.227186Z","title":"Bernstein inequality and moderate deviations under strong mixing conditions","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:32.227186Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:3bd482d3ed09cd615606788fd965c27c364490b3e543fd3f88d04b80783fa022","observation_id":"ababa613-e2f2-48c9-8a82-98fd7d1cb771","resolution":{"observed_at":"2026-08-01T10:18:32.227186Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:32.371990Z","title":"Smooth function approximation by deep neural networks with general activation functions","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:32.371990Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:e4112922007bc90800d9988a1cc57fa8354f581111f5a21565db18063a7eb336","observation_id":"824269d2-0f28-4c2f-a718-1f3c2a7139c1","resolution":{"observed_at":"2026-08-01T10:18:32.371990Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:32.534626Z","title":"Nonconvex sparse regularization for deep neural networks and its optimality","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:32.534626Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:910779ada396ccc95874f2dfb793aa018df04ddf0c8b03f1f20813dde5f62b47","observation_id":"28e5e5c8-7575-4f57-930b-f170f6a03b5b","resolution":{"observed_at":"2026-08-01T10:18:32.534626Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:32.669230Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:32.669230Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:6dd9382218bf1538f4a5930642abf9bd4edfc3889ff8dedfb166348a64a3b2b1","observation_id":"0290d046-3318-4719-9b0c-aa99799bf3b1","resolution":{"observed_at":"2026-08-01T10:18:32.669230Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:32.806712Z","title":"Optimal approximation of piecewise smooth functions using deep relu neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:32.806712Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:ffd22f488798ae9b1b14cd14eabce595f3ce6551691b110d7f4271df34a1044a","observation_id":"7b74de75-aed5-4aa3-a20a-d1cbafa93c45","resolution":{"observed_at":"2026-08-01T10:18:32.806712Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:32.918823Z","title":"W., Cannings, T","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:32.918823Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:1b77db9d0bea2bdb057394c856f1b706f2059ca23aa0d1ccdb2856854aaf8ab1","observation_id":"71f97c17-8b77-42b3-a958-67092203ecb6","resolution":{"observed_at":"2026-08-01T10:18:32.918823Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:33.044760Z","title":"Concentration of measure inequalities for markov chains and -mixing processes","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.044760Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:1f7de620020bf6f68723ee4b53c02a70f3fe64cde0e9d355ba13906a19f78991","observation_id":"edd0ed62-cb2e-47f3-8c84-3f46c1ec1214","resolution":{"observed_at":"2026-08-01T10:18:33.044760Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:33.154898Z","title":"Nonparametric regression using deep neural networks with relu activation function","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.154898Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:9fcbf6bd4f8769cbc0500fb549f7501384c5437214382711cb15282265bf20e7","observation_id":"42302bd3-5d09-4367-ba6c-20d6268a3bfe","resolution":{"observed_at":"2026-08-01T10:18:33.154898Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:33.309241Z","title":"Local convergence rates of the nonparametric least squares estimator with applications to transfer learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.309241Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:c9654a1e4ec2054cf153733a475fe929d94f96e28ffe4de05b569de2ab89523b","observation_id":"4f3cba7c-fdc0-4327-9e3d-7f01bbf7a65b","resolution":{"observed_at":"2026-08-01T10:18:33.309241Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:33.395288Z","title":"L., and Huang, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.395288Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:7cc3d856c5ffa71d850f9161b85cae3b023d6375949eff2106f5eab860ffa32e","observation_id":"a74032aa-f315-4ec4-a767-ea6f13a3c26b","resolution":{"observed_at":"2026-08-01T10:18:33.395288Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:33.517192Z","title":null,"venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.517192Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:1d886f6009c212e7ee9ec0f64f79daae830730a71ab1cd8e21db2b9d6241bb76","observation_id":"49dcdaa4-0962-4a3e-8bcc-b1bd8e7d770b","resolution":{"observed_at":"2026-08-01T10:18:33.517192Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:33.629047Z","title":"Measuring robustness to natural distribution shifts in image classification","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.629047Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:8bd09e4795f0598509e49a969d6ea35a4a5c02602659635f8735074458cdb606","observation_id":"4620d2cf-09b1-4282-80eb-13adeb9d40bc","resolution":{"observed_at":"2026-08-01T10:18:33.629047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.13591","last_updated":"2025-03-16T04:39:42Z","snapshot_observed_at":"2026-08-12T22:58:08.850219Z","submitted_at":"2024-08-24T14:26:09Z","title":"Optimal Kernel Quantile Learning with Random Features","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.13591","snapshot_observed_at":"2026-08-01T10:18:33.688272Z","title":"Optimal kernel quantile learning with random features","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.688272Z"},"links":{"cited_paper":"/paper/2408.13591","citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:fe75456fb6ca07d43114d109433509df5fd21af235be0bc084948f1ec97a8497","observation_id":"ed0743a8-aa8d-4618-9cce-04c8c81634da","resolution":{"observed_at":"2026-08-01T10:18:33.688272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.24854","last_updated":"2026-05-24T04:17:14Z","snapshot_observed_at":"2026-08-01T01:32:02.432749Z","submitted_at":"2026-05-24T04:17:14Z","title":"Deep Regression for Repeated Measurements under Covariate Shift","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.24854","snapshot_observed_at":"2026-08-01T10:18:33.763745Z","title":"Deep regression for repeated measurements under covariate shift","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.763745Z"},"links":{"cited_paper":"/paper/2605.24854","citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:3576db39ea9afb987a43bb81a148e5c1ec80d9d06419cd62b1fe8179f8209940","observation_id":"1eeec1e6-0630-4d1f-9242-726706197275","resolution":{"observed_at":"2026-08-01T10:18:33.763745Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:33.840978Z","title":"A minimax theory of nonparametric regression under covariate shift","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.840978Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:b8f6fcd084e154bcc9fd07f39bfa9effcb2b3be5568a311181c00c0d80e05fa8","observation_id":"2d7eb9cb-5f11-4b13-8a4e-51411dff15fa","resolution":{"observed_at":"2026-08-01T10:18:33.840978Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:33.977136Z","title":"Nearly unbiased variable selection under minimax concave penalty","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:33.977136Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:5af1c909f2f59c6f0286a1542a173fbd8bb47e20ce60a60dea24230f76c469e1","observation_id":"8e97cbdb-5bc8-4ba1-9c3c-be890729f8e5","resolution":{"observed_at":"2026-08-01T10:18:33.977136Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:34.094506Z","title":"Analysis of multi-stage convex relaxation for sparse regularization","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:34.094506Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:b2fb927e4564dccb1bf0f7bedd9841032ab935c867a099384d4889f4a037fa23","observation_id":"99a2b28d-78dc-41c5-8f6d-3065ec33aacc","resolution":{"observed_at":"2026-08-01T10:18:34.094506Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T10:18:34.186208Z","title":"Effective number of observations and unbiased estimators of variance for autocorrelated data-an overview","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-01T10:18:34.186208Z"},"links":{"citing_paper":"/paper/2607.20309"},"observation_digest":"sha256:3ad5bf80b61f7a1ef721119f2aade781be40558926bc6eeca845dc385254613f","observation_id":"84c86690-4797-4735-a4b6-3f1545ace3b5","resolution":{"observed_at":"2026-08-01T10:18:34.186208Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.20309","last_updated":"2026-07-22T15:55:46Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-12T05:31:27.925351Z","submitted_at":"2026-07-22T15:55:46Z","title":"Adaptive deep nonparametric regression from dependent data under covariate shift"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":49,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":49},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2607.20309."}