{"as_of":"2026-08-10T14:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ef1c8c5b4d51a064e3a4e8e3e4a1a759dcdfe20894093afd6e32c05e95c24ea0","coverage":[{"denominator":67,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":67,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T15:42:30.613683Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-13T06:30:51.812541Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-13T06:32:24.257088Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"cited_work":{"arxiv_id":"2502.01347","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.01347","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2502.01347 , year=","venue":null,"work_id":"dbc90e62-74c0-42c5-93c6-c9a2e94b1b42","year":null},"citing_paper":{"arxiv_id":"2605.11134","last_updated":"2026-05-29T17:16:57Z","snapshot_observed_at":"2026-08-01T16:30:31.998321Z","submitted_at":"2026-05-11T18:41:12Z","title":"Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-13T06:30:51.812541Z"},"links":{"cited_paper":"/paper/2502.01347","citing_paper":"/paper/2605.11134"},"observation_digest":"sha256:1d16f1b5dda5c2712f3855e454bc4c60dc29175501e03e17846fc84b7ec0947d","observation_id":"de6f8656-fc04-42cd-bf4c-5ee248565783","resolution":{"observed_at":"2026-05-13T06:32:24.258643Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.01347/citation-record","integrity":"/paper/2502.01347/integrity","json":"/paper/2502.01347/citation-record.json","paper":"/paper/2502.01347"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:42:32.311975Z","title":"Systematic generalisation with group invariant predictions","venue":null,"work_id":"1b486160-4ffe-4747-88c7-c4d05fd9027f","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.290084Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:28a63c9f26ff411150f499d1608afa6cf37eb805b9c8b582b80e46072dd01e7f","observation_id":"60f7d443-1bdc-41b2-8475-365a317d71be","resolution":{"observed_at":"2026-08-09T15:42:32.316953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02893","last_updated":"2020-03-27T19:07:58Z","snapshot_observed_at":"2026-07-06T08:05:24.076802Z","submitted_at":"2019-07-05T15:26:26Z","title":"Invariant Risk Minimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.02893","snapshot_observed_at":"2026-08-09T15:42:30.295651Z","title":"Invariant risk minimization","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.295651Z"},"links":{"cited_paper":"/paper/1907.02893","citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:b77fbbd47fd7fe8b1b2762fec9d0fdeb0c012eeb324fb1bf58d27582f846d4cb","observation_id":"96cf9837-398f-4fc5-ba81-f777291cff04","resolution":{"observed_at":"2026-08-09T15:42:30.295651Z","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":"2026-08-09T15:42:32.295862Z","title":"High-dimensional asymptotics of feature learning: How one gradient step improves the representation","venue":null,"work_id":"08ff6f2b-e120-4d46-a1fd-50d4115cd02d","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.301221Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:4f1f0567716ec2b4c79db27220c52e5934670ade437b4d2b8705e5829534f3b5","observation_id":"a7995196-6fe3-4fb6-bd54-b4fde7eaf760","resolution":{"observed_at":"2026-08-09T15:42:32.301291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:32.279709Z","title":"Deep learning: a statistical viewpoint","venue":null,"work_id":"15ac0741-3475-40ac-9b70-bae2a8eab07d","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.306094Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:6c08327775f2fb83ff48bda622fb4b82469f382d294561b1b6c5edf90a931392","observation_id":"377d5e3e-1f58-4629-9f86-4a4259855857","resolution":{"observed_at":"2026-08-09T15:42:32.284905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:30.311527Z","title":"Reconciling modern machine-learning practice and the classical bias–variance trade-off.Proceedings of the National Academy of Sciences, 116(32):15849–15854, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.311527Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:b6caac08b0ea753972165cb4190c2adcf6acce387b34d9520d14c1f70ecfe089","observation_id":"37cdac80-960f-4a6d-a75b-60ed2fc466b4","resolution":{"observed_at":"2026-08-09T15:42:30.311527Z","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":"2026-08-09T15:42:32.241065Z","title":"Memorization and optimization in deep neural networks with minimum over-parameterization","venue":null,"work_id":"efe98cf1-300c-4b43-8c80-fedc386dc667","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.316214Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:73246c75cb48c313afab002586d9b6552b926975489aff87154b69d58186ceb6","observation_id":"3be64fce-456c-4fce-b940-7e8353fb5ce4","resolution":{"observed_at":"2026-08-09T15:42:32.258895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:32.148468Z","title":"Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels","venue":null,"work_id":"427b634e-e73d-4c90-86d8-c4f396dc20be","year":2023},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.321379Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:ad828fb06d1c82dfd580c136c303d1402dff590fdad8eee488a79da2fe4481a9","observation_id":"3630a444-cdea-47b3-96e6-1726c9cd5749","resolution":{"observed_at":"2026-08-09T15:42:32.190463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14787","last_updated":"2025-05-27T20:36:47Z","snapshot_observed_at":"2026-08-10T11:25:02.843968Z","submitted_at":"2024-10-18T18:01:11Z","title":"Privacy for Free in the Overparameterized Regime","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14787","snapshot_observed_at":"2026-08-09T15:42:30.325967Z","title":"Privacy for free in the over-parameterized regime.arXiv preprint arXiv:2410.14787, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.325967Z"},"links":{"cited_paper":"/paper/2410.14787","citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:29470e78da18a180df8a1f484260df9ffc803075578b7b162b75d04906b82aa8","observation_id":"3cd5d8ab-3de6-4fed-a5eb-5b0b8b541bc5","resolution":{"observed_at":"2026-08-09T15:42:30.325967Z","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":"2026-08-09T15:42:32.029458Z","title":"A universal law of robustness via isoperimetry","venue":null,"work_id":"9974e321-a5bf-49fa-a8b7-5f3fc4e0d8dc","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.330831Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:981936f2ad93068f2150700f9b5329c4e9b0c611b656072efc6703e9ecca7e9c","observation_id":"14caf4e1-c708-467b-9508-e92f682d1205","resolution":{"observed_at":"2026-08-09T15:42:32.092617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.964870Z","title":"Chang, G","venue":null,"work_id":"dd12f23b-28e2-43d8-918b-deb1883a8efd","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.335456Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:9641bd24c40ead0c02729f364cabbdca8180edb311d4497ead024ae7463d5cdc","observation_id":"2376d210-4b8f-4eda-a1de-b5edd8794124","resolution":{"observed_at":"2026-08-09T15:42:31.970971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.949659Z","title":"Provable benefits of overparameterization in model compression: From double descent to pruning neural networks","venue":null,"work_id":"4373d7c5-2ffc-4f37-8a36-10ed13ef1444","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.340320Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:8ad99e7e2648f13bacba267305cf9a94b7d6a45a172cbc5ca9cdd31bad186f81","observation_id":"c24d0ef4-abf7-4d8e-9903-ef1717d3809f","resolution":{"observed_at":"2026-08-09T15:42:31.954495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.912947Z","title":"Dimension free ridge regression.The Annals of Statistics, 52(6):2879 – 2912, 2024","venue":null,"work_id":"0bee695f-6d31-4436-b136-b912f0ab836b","year":2024},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.345077Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:0006f94aaf83c0befdf409c879364ef3a9fea3e4c45203cf2924235fb2b1b5b5","observation_id":"60290383-7d32-474a-9c6e-34b21ae7b44d","resolution":{"observed_at":"2026-08-09T15:42:31.928922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.897689Z","title":"Neural networks can learn representations with gradient descent","venue":null,"work_id":"ce5c1ba7-bdb5-4762-9992-6104170974e2","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.350172Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:30ac44841b83620d2b9575f64e8e356164439feb5d48d73b8098bd471679c846","observation_id":"5d423cdc-b8bf-41fc-9e5d-445f2f61a508","resolution":{"observed_at":"2026-08-09T15:42:31.902502Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.11864","last_updated":"2022-07-04T12:05:11Z","snapshot_observed_at":"2026-08-07T22:34:17.362654Z","submitted_at":"2022-03-22T16:40:52Z","title":"On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes","version":2},"cited_work":{"arxiv_id":"2203.11864","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.11864","snapshot_observed_at":"2026-08-09T15:42:31.115579Z","title":"On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes","venue":"stat.ML","work_id":"3a0e09a0-ef95-44a2-a8ab-3f68a2f78c4f","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.354802Z"},"links":{"cited_paper":"/paper/2203.11864","citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:6221852ed40b6d9b45c82cc2fa7c11e0e71d9c07570509f961155ce2f33c22a0","observation_id":"a8a546d0-e760-4256-ae10-a79208e2cba8","resolution":{"observed_at":"2026-08-09T15:42:31.123509Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:30.359752Z","title":"Wichmann","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.359752Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:dbdf1a16554630bb64b50164d4185c9d3ad1234fc423cdd70e8b34a71604c9db","observation_id":"55642ab9-5e5d-4e69-ba0c-120764d1cf5c","resolution":{"observed_at":"2026-08-09T15:42:30.359752Z","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":"2026-08-09T15:42:31.871695Z","title":"Wichmann, and Wieland Brendel","venue":null,"work_id":"eaa3d9f9-7305-4095-abf0-b29eaa600c3d","year":2019},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.364021Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:047b0025c0b2fbca177365dbfdb8d35f226f58b5b6f03a9c0dd676b5d0b1d4cb","observation_id":"6c327376-df14-46e1-bd93-6b9f33b10920","resolution":{"observed_at":"2026-08-09T15:42:31.876936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.856878Z","title":"The gaussian equivalence of generative models for learning with shallow neural networks","venue":null,"work_id":"9fa4c406-4a7c-4f73-b152-b11bf2d8d891","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.368728Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:a0e261920dd2a4b9c380c3bb9f60ad85a198773558e684c4afd315bcc5d85b71","observation_id":"f632a93e-c739-40fe-9028-a51972fdad9c","resolution":{"observed_at":"2026-08-09T15:42:31.861515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:30.373341Z","title":"Modeling the influence of data structure on learning in neural networks: The hidden manifold model.Physical Review X, 10(4):041044, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.373341Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:e7032c9da0c0f607e7b1beff0b7f73342c8fa2590d66d63ebc89662a4c63073b","observation_id":"e091a4c2-0f22-41ef-86b3-d7abfc2fe278","resolution":{"observed_at":"2026-08-09T15:42:30.373341Z","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":"2026-08-09T15:42:30.377932Z","title":"The distribution of ridgeless least squares interpolators.arXiv preprint arXiv:2307.02044, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.377932Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:923645bcc14219c821bdbac105b9f0455379192980d184af97a083aac6f09c27","observation_id":"37068ab9-fe1b-4c77-b5d0-ac4e0e7cc98d","resolution":{"observed_at":"2026-08-09T15:42:30.377932Z","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":"2026-08-09T15:42:31.831922Z","title":"The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression.The Annals of Statistics, 52(2):441 – 465, 2024","venue":null,"work_id":"3a087194-3715-4d76-861b-dea9e434de6b","year":2024},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.382515Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:56d0a672fb73098cfbf7fa87fb0c1a55adf3961413fb77f75d3e6b21b9c1b944","observation_id":"ca6a6485-53e2-45ae-9d84-536d04f17766","resolution":{"observed_at":"2026-08-09T15:42:31.836993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.816357Z","title":"Hastie, Andrea Montanari, Saharon Rosset, and Ryan J","venue":null,"work_id":"1973b2e4-c3d1-41fc-ab49-aca39b2a73ac","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.387546Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:f740fe72fbbd5a1c8efc3e124d23eb0fa2b57986b72702d709f865e368daa40c","observation_id":"95407e10-2abc-4eb2-bab0-a74b79748cb6","resolution":{"observed_at":"2026-08-09T15:42:31.821379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.801063Z","title":"What shapes feature representations? Exploring datasets, architectures, and training","venue":null,"work_id":"4de4e873-6b45-4a0e-8f1d-d3c340b528ae","year":2020},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.392454Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:831714958dc18eedcba23165f8b86034bf99b7cbfb0c817d8ce256f14a1c9ca2","observation_id":"7f6fb99a-5510-4590-823e-0dacb48da18c","resolution":{"observed_at":"2026-08-09T15:42:31.805799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.785454Z","title":"On the foundations of shortcut learning","venue":null,"work_id":"10b08fbe-7f3f-4ce0-935b-4ff93da098cf","year":2024},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.397212Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:f09f90eb577c81144d891d4c72e1dc9d5546a3af05e377957b9819ccd6433315","observation_id":"75b92543-6737-443c-bb77-5faa1b38bc5d","resolution":{"observed_at":"2026-08-09T15:42:31.790233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:30.401609Z","title":null,"venue":null,"work_id":null,"year":1932},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.401609Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:c180c3b51f19bfaa3b3aeaed3810e6917f62e3748f00241c66fea6b47373b969","observation_id":"528c0e9e-6148-458d-875a-97e5de275c6b","resolution":{"observed_at":"2026-08-09T15:42:30.401609Z","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":"2026-08-09T15:42:31.761110Z","title":"On feature learning in the presence of spurious correlations","venue":null,"work_id":"afa0b405-6d38-4620-95dd-a6739df515ad","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.406394Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:47ca9f3c2b18e87e2e02f8c109dda182fcb754e461835f5de275fc82606d0c1a","observation_id":"a1a92ab5-3e3a-4605-813d-e2a1659f4bfa","resolution":{"observed_at":"2026-08-09T15:42:31.765792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.745563Z","title":"Sgd on neural networks learns functions of increasing complexity","venue":null,"work_id":"4e47e966-3041-483b-9769-ed5a38af91e9","year":2019},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.411227Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:4f950fdc26061d321a152d1b770f238490e778a9234fda7dc0f9170edd85fe4e","observation_id":"05ee59b9-8771-49df-8bdd-66e599b20e02","resolution":{"observed_at":"2026-08-09T15:42:31.750385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.729845Z","title":"Last layer re-training is sufficient for ro- bustness to spurious correlations","venue":null,"work_id":"8e4b214c-d802-4369-8873-667216c0e2d5","year":2023},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.416073Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:361a38fc2d7d0cd55989d349229047a07e9b8205850e12b3972221dc526db35b","observation_id":"e0e8b0d2-29c7-4d83-9c01-8d57211dd602","resolution":{"observed_at":"2026-08-09T15:42:31.734853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.714688Z","title":"Demystifying disagreement-on-the-line in high dimensions","venue":null,"work_id":"c58bd872-d56e-43f4-bea6-0a4299a4281d","year":2023},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.421211Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:d39daf3e640307dc6bd58fb944a53094385e105ebd7671ba9e8ba41ce60d0a69","observation_id":"eda4bb10-2186-409b-8dc3-fb2659fb2c69","resolution":{"observed_at":"2026-08-09T15:42:31.720098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.696663Z","title":"Just train twice: Improving group robustness without training group information","venue":null,"work_id":"1f2eeda8-abc9-4dd8-87c7-193e7b6e77b3","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.425945Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:e0b81d974a76c47a30f3e5b8c2d635f1c86038f96c7148f2c6671103a4fec4c2","observation_id":"cb14d96d-49d5-487f-96b8-f800cc0d4a00","resolution":{"observed_at":"2026-08-09T15:42:31.704307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.680584Z","title":"Avoiding spurious correlations via logit correction","venue":null,"work_id":"8ed13872-e574-473f-a7c3-872981dc5b38","year":2023},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.430737Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:8ee8db321b80c2ea1fe14ea88fe7c170f45518aa24c9738bac836a11ebebd7ee","observation_id":"abc2ae63-e2f0-4743-b387-6bc31f073cb1","resolution":{"observed_at":"2026-08-09T15:42:31.685433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.664400Z","title":"Learning curves of generic features maps for realistic datasets with a teacher-student model","venue":null,"work_id":"7063134a-52b7-4b8b-b5fd-2fa71734dd56","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.435366Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:2574178e1b88fe0b26d01ecee3f241130ac91a8dd4b1dc08432a2c3bf187b525","observation_id":"9c2553f4-568e-4bda-bfb5-10f5663d0d4a","resolution":{"observed_at":"2026-08-09T15:42:31.669389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.649663Z","title":"Minimum-norm interpolation under covariate shift","venue":null,"work_id":"1dc94993-7fea-4b13-8dd6-c6889702a0ad","year":2024},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.440224Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:6990b58361f21d77351c77f479ab956150836b884602887e6734a5cfaa20025f","observation_id":"6579aa41-dacd-40a0-8c14-304dfd7cc165","resolution":{"observed_at":"2026-08-09T15:42:31.654469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.634347Z","title":"Generalization error of random feature and kernel methods: Hypercontractivity and kernel matrix concentration.Applied and Computational Harmonic Analysis, 59:3–84, 2022","venue":null,"work_id":"bb8c44bd-7328-44ec-83d2-cd16b7b0bd2d","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.444958Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:1e0909cadf5267efdc3f2a528eaedb8e1f70accbf192cd41e288c20b08c68b2a","observation_id":"1ddf58d4-bfa5-4e11-8e1d-b9242f24b7e0","resolution":{"observed_at":"2026-08-09T15:42:31.639175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.619128Z","title":"The generalization error of random features regression: Precise asymptotics and the double descent curve.Communications on Pure and Applied Mathematics, 75(4):667– 766, 2022","venue":null,"work_id":"f0830f7a-436a-4ccc-bda4-9247bc47b154","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.449766Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:94879979f1462e70afecc08f58c45322050eb02a160b6be4c9ac51bcb7baf09e","observation_id":"b04f7f3b-00a0-402b-9bde-560d6e8ca0fa","resolution":{"observed_at":"2026-08-09T15:42:31.624063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.603368Z","title":"Hard imagenet: Segmentations for objects with strong spurious cues","venue":null,"work_id":"265f67d8-381b-4e57-946b-ba262af5426c","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.454402Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:e04c9ca86cedb2c2bbbead58c39f820edfd94a19418d64889303a51b8fae9a7b","observation_id":"20a5008b-db31-40df-af4a-632015a2245e","resolution":{"observed_at":"2026-08-09T15:42:31.608530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.587739Z","title":"A theory of non-linear feature learning with one gradient step in two-layer neural networks","venue":null,"work_id":"98ab2166-09dd-464e-a675-09a828bbffdc","year":2024},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.459008Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:064bd278c456e83b9092cdeec23374e636330f35987067c95d90540c564f5f98","observation_id":"2a3ca3f0-6f7d-4220-b681-5c4c074e68e1","resolution":{"observed_at":"2026-08-09T15:42:31.592785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.01544","last_updated":"2023-03-22T16:53:25Z","snapshot_observed_at":"2026-08-10T11:27:40.320564Z","submitted_at":"2019-11-05T00:15:27Z","title":"The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.01544","snapshot_observed_at":"2026-08-09T15:42:30.464042Z","title":"The generalization error of max-margin linear classifiers: Benign overfitting and high dimensional asymptotics in the overparametrized regime","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.464042Z"},"links":{"cited_paper":"/paper/1911.01544","citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:7ae344d4ebf22faa6ba1552f6c2fad8cad34a6cdeecf6c7b22ef59de469b9837","observation_id":"d007c2da-88e4-4a14-829d-2f82c34bb6b2","resolution":{"observed_at":"2026-08-09T15:42:30.464042Z","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":"2026-08-09T15:42:31.572924Z","title":"Universality of empirical risk minimization","venue":null,"work_id":"eb01bb6a-6520-4e3d-be65-56b9aa89bf25","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.469581Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:322759a390575c002abaa324a48738d174dd975ebcd875757e9454dbdbf08340","observation_id":"418a0504-5246-481c-b634-babd79c8b861","resolution":{"observed_at":"2026-08-09T15:42:31.577422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.557503Z","title":"Simplicity bias in 1-hidden layer neural networks","venue":null,"work_id":"7c03393f-a8b7-42fe-a52a-8b8f89d03aca","year":2023},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.474639Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:9ac5d8fe0e54d3c5f606b45c1b6c9f6696c81c1b4f799e6a77056dee987c7445","observation_id":"5a5f0571-8574-489d-b570-5ba62cd5eef3","resolution":{"observed_at":"2026-08-09T15:42:31.562483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.541093Z","title":"Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks","venue":null,"work_id":"da4cc474-db68-460b-bd47-ba46bf2bf05b","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.479548Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:726ef32e1e008b02fe159f09f57a84b74fa9c0f0cae88e1f6b55cba3d789d151","observation_id":"fab760b7-5416-48b3-baaa-0bad2bac01ae","resolution":{"observed_at":"2026-08-09T15:42:31.546584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.524895Z","title":"Analysis of Boolean Functions","venue":null,"work_id":"ff56c2b3-82a2-4bb8-91b7-b69b20eb7d39","year":2014},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.484645Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:440c59432dea305ce46bad41d9d00d80518a76cee616f621e5d86585fdb42613","observation_id":"d4152023-78f1-4dac-9f9f-357a37ee31ca","resolution":{"observed_at":"2026-08-09T15:42:31.529683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.509122Z","title":"Gradient starvation: A learning proclivity in neural networks","venue":null,"work_id":"f0990f3b-7707-4723-8736-5fdeab710c10","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.489545Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:e229b29030afbe8d5478c67bdeb0d43a7423dd0ad9007edad03bb2edf1a086a9","observation_id":"bf6a3a26-59e5-4c1f-9d1c-cccab56c7d18","resolution":{"observed_at":"2026-08-09T15:42:31.514071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.492873Z","title":"Finding and fixing spurious patterns with explanations","venue":null,"work_id":"c70fc728-b4bf-4c16-a284-df0434deaa2f","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.494493Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:7a1ac4f152124b4b7eda894fb6cc1b026f1957d49e2018a05b824b635ec653be","observation_id":"45f53c0a-9c9c-40b7-9a46-0f3d16565daf","resolution":{"observed_at":"2026-08-09T15:42:31.497998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.476444Z","title":"Complexity matters: Dynamics of feature learning in the presence of spurious correlations","venue":null,"work_id":"9236f0d5-6cf5-410e-9204-1a1bf08e536e","year":2024},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.499119Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:13cce955becfa39fb18a45c284999e4979214998696a96ffe2aac4d9f4b7b7e4","observation_id":"05b5606e-565b-41a1-ba66-4fdea70ca1c6","resolution":{"observed_at":"2026-08-09T15:42:31.481575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.459607Z","title":"On the spectral bias of neural networks","venue":null,"work_id":"febc904e-daf5-45ce-93c6-b0b667fc31aa","year":2019},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.504050Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:5013da06cd5270bb28f9a15f7069ba500a95e135f975379d6c69e3cba2341c01","observation_id":"0f9c0b59-26bd-4e45-b72a-0af5973842a7","resolution":{"observed_at":"2026-08-09T15:42:31.465430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:30.508719Z","title":"Random features for large-scale kernel machines","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.508719Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:bbe8f39778c5be650722c13a3284d3e2548a38deb95b5153b83ab373bab5c4cd","observation_id":"abd4bbbd-3b24-49c5-97b6-d093fe5e2bf4","resolution":{"observed_at":"2026-08-09T15:42:30.508719Z","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":"2026-08-09T15:42:30.513423Z","title":"Early stopping and non-parametric regression: an optimal data-dependent stopping rule.The Journal of Machine Learning Research, 15(1):335–366, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.513423Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:7857eed3ff3529acbaef937fc982cb816a47e00c198bc5900dd867375f591093","observation_id":"38dc703e-f29e-4a68-befd-4c5803ae26a8","resolution":{"observed_at":"2026-08-09T15:42:30.513423Z","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":"2026-08-09T15:42:30.518196Z","title":"Hashimoto, and Percy Liang","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.518196Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:78602e61488a74ddf7895d8e0dda5f53ef09102a66125dc5c67ab9fabb3d7b2c","observation_id":"3c70ec90-4188-4895-ac2e-1b1158cdc08b","resolution":{"observed_at":"2026-08-09T15:42:30.518196Z","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":"2026-08-09T15:42:31.411834Z","title":"An investigation of why overpa- rameterization exacerbates spurious correlations","venue":null,"work_id":"e89352a1-5343-4c57-975e-7e4280bb1242","year":2020},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.522793Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:e7defdd9b5c813898828fc28e6283a6fe34dc627ad997f115eec1b578a7dcf3d","observation_id":"32c078ed-6e0e-4511-8f5d-9824298c6de0","resolution":{"observed_at":"2026-08-09T15:42:31.416798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.395899Z","title":"Information-theoretic bias reduction via causal view of spurious correlation","venue":null,"work_id":"00aaed7b-419c-43be-ad8a-423b4c7af970","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.527678Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:537bc915b0f3c0643831d019a395b1ed24109079328184aa32fa115cf4466822","observation_id":"687980b3-12aa-4f26-91f9-e2d1d7745f6f","resolution":{"observed_at":"2026-08-09T15:42:31.400756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.380554Z","title":"The pitfalls of simplicity bias in neural networks","venue":null,"work_id":"9a0f82c6-b178-42f9-bf2a-551833fc4b27","year":2020},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.532511Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:8acb816aa2c4c875f5308320c3908b2e4ae739af111fe10dc0b759f14fd0865e","observation_id":"31276436-ac0b-4cec-ac14-e46bbd661237","resolution":{"observed_at":"2026-08-09T15:42:31.385396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.365301Z","title":"Salient imagenet: How to discover spurious features in deep learning? In International Conference on Learning Representations, 2022","venue":null,"work_id":"e5ddcf1c-24cf-4a6e-bae3-2eec97f89c39","year":2022},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.537443Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:047e3286bef75da400af17b3a20af14958e2cbad198fd9ee3a333aec652b56fb","observation_id":"4a5562e1-2f0c-40a0-a422-1d2d26bdbfec","resolution":{"observed_at":"2026-08-09T15:42:31.370197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.13944","last_updated":"2026-06-27T02:08:29Z","snapshot_observed_at":"2026-08-06T20:36:35.792109Z","submitted_at":"2024-06-20T02:23:28Z","title":"Generalization error of min-norm interpolators in transfer learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.13944","snapshot_observed_at":"2026-08-09T15:42:30.542667Z","title":"Generalization error of min-norm interpolators in transfer learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.542667Z"},"links":{"cited_paper":"/paper/2406.13944","citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:44dd1fe1903155e484568968aab9afcba95d7f63bc6a7c1b178fe0776b1bcf7b","observation_id":"595463b1-cc7c-4074-8bd9-76da87c4a5e3","resolution":{"observed_at":"2026-08-09T15:42:30.542667Z","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":"2026-08-09T15:42:31.349088Z","title":"Regularized linear regression: A precise analysis of the estimation error","venue":null,"work_id":"2942af3d-62b7-4177-a885-48ed6b3001d2","year":2015},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.547801Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:f4e9c45b063ddf2e9ed627ae5021d23175a826a98fc94070cbe16052a0bac7f2","observation_id":"852011dd-2f5f-4fbf-809b-f960a06a0f37","resolution":{"observed_at":"2026-08-09T15:42:31.354510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.330906Z","title":"Overcoming simplicity bias in deep networks using a feature sieve","venue":null,"work_id":"452125f3-262f-47a0-bc5c-557a9a257340","year":2023},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.552511Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:9a308a8a38b0c40d26a0e0d4030a10ab2fb3304917c9733a38703c26076729d8","observation_id":"a2bf2751-3b4f-4f2b-9e2e-d924399b7a0d","resolution":{"observed_at":"2026-08-09T15:42:31.336872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.313486Z","title":"Overparameterization improves robustness to covariate shift in high dimensions","venue":null,"work_id":"aedce059-0117-4bf2-a91b-65f6e9b5136b","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.557078Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:279264300650817d415e2cd98602213bc4c3cc4db9c4114a1db80db0ccf7c9c6","observation_id":"11dcb660-8231-4679-a47a-5b0619a9142e","resolution":{"observed_at":"2026-08-09T15:42:31.318816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.296887Z","title":"Counterfactual invariance to spurious correlations in text classification","venue":null,"work_id":"86440fdf-a31f-413f-aefb-df7ed9427624","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.561976Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:4de2ed7765932477321969ddd9a86898783a798985437ba1e1c66a38fc0661ab","observation_id":"e9795b80-9abe-44ce-93b4-e3d2e0403983","resolution":{"observed_at":"2026-08-09T15:42:31.302107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.279968Z","title":"Introduction to the non-asymptotic analysis of random matrices, page 210–268","venue":null,"work_id":"d74c49c4-97d3-4d27-b616-f766922320e2","year":2012},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.567725Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:57ea6d685210679b6b8e4e2454ea932c7686929c0bd76df5a332b98112bd93dd","observation_id":"d6ed948f-ff06-464b-813a-7a6b460ad1fb","resolution":{"observed_at":"2026-08-09T15:42:31.285426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.262885Z","title":"High-dimensional probability: An introduction with applications in data science","venue":null,"work_id":"86405a2b-b282-4517-9cef-52a73ed236be","year":2018},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.572918Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:59382eedce9125439ef4cfd965eb9ccf49a971e06adee7c329c0d5f9542d570b","observation_id":"7115f763-0a1b-47a6-9ceb-15af64b80555","resolution":{"observed_at":"2026-08-09T15:42:31.268571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.245701Z","title":"Noise or signal: The role of image backgrounds in object recognition","venue":null,"work_id":"a829979c-9146-440c-bde0-6644993b036a","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.577956Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:c41cb5a0e047eced373fd18a6fc4908cfad7e8f855d7ee501a3602c573156142","observation_id":"38d8fefc-1ee5-4da6-a1c8-05717e2fd321","resolution":{"observed_at":"2026-08-09T15:42:31.250934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11750","last_updated":"2025-06-09T23:26:52Z","snapshot_observed_at":"2026-07-06T10:07:15.228239Z","submitted_at":"2020-10-22T14:14:20Z","title":"Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11750","snapshot_observed_at":"2026-08-09T15:42:30.582715Z","title":"Zhang, Sen Wu, Christopher Ré, and Weijie J","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.582715Z"},"links":{"cited_paper":"/paper/2010.11750","citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:6c1faf207b92845180d52b3231aabbbc81607d0140e859d2936da4dda70707a6","observation_id":"39e869ca-e0c4-4319-9646-57195742ba02","resolution":{"observed_at":"2026-08-09T15:42:30.582715Z","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":"2026-08-09T15:42:30.587883Z","title":"Spurious correlations in machine learning: A survey.arXiv preprint arXiv:2402.12715, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.587883Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:0ccda550084e747f68fa85142948071f9bac4c172ac4facef9d29c18aa1036d5","observation_id":"f24aeb67-7553-4b2b-8b74-9fc240f3e07c","resolution":{"observed_at":"2026-08-09T15:42:30.587883Z","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":"2026-08-09T15:42:30.592669Z","title":"Coping with label shift via distributionally robust optimisation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.592669Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:04abf73d2b7bffda86972c1a8cc46b46195e2af68d1c6320f76d5c90eab50c49","observation_id":"e0eb20a4-2449-4849-9429-05bbbf6a8bb5","resolution":{"observed_at":"2026-08-09T15:42:30.592669Z","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":"2026-08-09T15:42:31.218289Z","title":"Examining and combating spurious features under distribution shift","venue":null,"work_id":"07ebf856-737c-44b0-b53e-95c05faf88d8","year":2021},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.597526Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:6c572ab4a82bec369e9128cab411798a579feebc667b6f38f2d52361058e52a5","observation_id":"a4970b69-33ee-48c3-8fa8-87b15b67fa56","resolution":{"observed_at":"2026-08-09T15:42:31.223791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.202297Z","title":"On the relation between accuracy and fairness in binary classification","venue":null,"work_id":"e4b880a3-4173-4bd3-9802-4843479f4d57","year":2015},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.602299Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:6cd73d8e5329a6fc30d71f15db2c1faba65318e2d4e851153cb8fedd66cec71d","observation_id":"9caa4f4e-5fa3-472b-b90c-5c0c14dc3ed3","resolution":{"observed_at":"2026-08-09T15:42:31.207379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.185607Z","title":null,"venue":null,"work_id":"36e4b635-d1fc-4248-87e2-5f03e0f397e6","year":null},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.608769Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:c236ea30811b046e68e67a0340ae3c2c2d75aeae65eb21d923c6ee162aaf82d7","observation_id":"bf700bda-1201-4b63-87f7-ae07cb5b121f","resolution":{"observed_at":"2026-08-09T15:42:31.190540Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-09T15:42:31.168498Z","title":"orthogonal features","venue":null,"work_id":"c804adb0-bc27-45cf-bd8a-01f442fb91e6","year":null},"citing_paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-09T15:42:30.613683Z"},"links":{"citing_paper":"/paper/2502.01347"},"observation_digest":"sha256:bb4c66036f3a89dc24479591a025820cd3aa446ecfc99a73678bd51a8c170773","observation_id":"6210e68d-c3be-420c-97fc-f8c518566d7f","resolution":{"observed_at":"2026-08-09T15:42:31.173480Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.01347","last_updated":"2025-05-27T20:47:48Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-10T12:12:04.905155Z","submitted_at":"2025-02-03T13:38:42Z","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization"},"reference_resolution":{"displayed":67,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":1,"verified_fuzzy":49},"total_outbound_references":67},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2502.01347."}