{"as_of":"2026-08-18T23:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a61e440e3f9efe52cf28ab7882543c01051884b3ffd5a08dd3bae463cb665aa2","coverage":[{"denominator":64,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":64,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:25:34.975992Z","state":"measured"},{"denominator":64,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":64,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.13273/citation-record","integrity":"/paper/2501.13273/integrity","json":"/paper/2501.13273/citation-record.json","paper":"/paper/2501.13273"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:36.094822Z","title":"A reductions approach to fair classification","venue":null,"work_id":"79e8d8e5-ebc4-42f4-b70b-a094d43d2b85","year":2018},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.603445Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:3c2ce8e515f06da99373e94a54e2262534e4ff4c291ba34248d5ba79298af0fc","observation_id":"6b128182-bc35-48a5-bac2-4514dc8d83bf","resolution":{"observed_at":"2026-08-10T16:25:36.102864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:36.072659Z","title":"Square attack: a query-efficient black-box adversarial attack via random search","venue":null,"work_id":"3c2beb35-35f4-4261-a291-7b50c24ba0b0","year":2020},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.608819Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:04f37a0bf94ff5e529f1f9766ecc658e8b0bb2061df30f45c17a2d4d180a0ffd","observation_id":"b1dfca0a-b712-4da3-9e40-2ab1e1b1b772","resolution":{"observed_at":"2026-08-10T16:25:36.080069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:34.614341Z","title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.614341Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:ae86c4c434cde2bf1530a1b1235cfac75edddf92b3a7387900c898b890a0bfb5","observation_id":"8e1e1800-f6f9-4d58-933f-feb4b61ffe2f","resolution":{"observed_at":"2026-08-10T16:25:34.614341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.02662","last_updated":"2021-08-20T21:19:16Z","snapshot_observed_at":"2026-08-16T18:33:31.380105Z","submitted_at":"2021-04-06T16:49:54Z","title":"The spectral norm of Gaussian matrices with correlated entries","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.02662","snapshot_observed_at":"2026-08-10T16:25:34.620257Z","title":"The spectral norm of gaussian matrices with correlated entries","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.620257Z"},"links":{"cited_paper":"/paper/2104.02662","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:fb1dcc470ab178abd12a00b5cd2d9fe2b4e2cc6bc7c2c21e3b78d545dd456d79","observation_id":"961c7d03-e15a-4810-a3d4-8129fb13c837","resolution":{"observed_at":"2026-08-10T16:25:34.620257Z","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":"2026-08-10T16:25:34.625317Z","title":"Spectrally-normalized margin bounds for neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.625317Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:704f1253258b3ec17f9cc6315d4ef62df3234925a73da44d04b50cffc43eed73","observation_id":"63206eb4-ca2f-43a3-9ce8-c70f89d6bdf0","resolution":{"observed_at":"2026-08-10T16:25:34.625317Z","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":"2026-08-10T16:25:36.015126Z","title":"Evasion attacks against machine learning at test time","venue":null,"work_id":"67d822ea-ad9a-4aa9-8395-27dbadf44486","year":2013},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.630554Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:0b7eb12ad4faecea31694cb85f891d259abf22d82d9085207a05ffa62c7289fd","observation_id":"d34bccfa-7a82-407a-bbcf-b766c459f272","resolution":{"observed_at":"2026-08-10T16:25:36.022645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.993442Z","title":"Towards evaluating the robustness of neural networks","venue":null,"work_id":"f8ee4107-3d29-4225-aa12-80a80fa90563","year":2017},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.636003Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:503f52a07b4f43fa73491eb42077f605853e1e95d54fdf83ac8425fe2386fa87","observation_id":"18744c28-7fe6-4fb2-ae3d-ecb51e3f2dc8","resolution":{"observed_at":"2026-08-10T16:25:35.999634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.969675Z","title":"Minimally distorted adversarial examples with a fast adaptive boundary attack","venue":null,"work_id":"7f93f5c2-8e75-46b8-a3bd-584f6d5b6095","year":2020},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.640354Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:775c65c8dbe00fc600b1707a283772a59a955db2ce0d38c36b7a493272555e5a","observation_id":"f4bd328c-82e4-4ceb-a666-a8f797c499ec","resolution":{"observed_at":"2026-08-10T16:25:35.979344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.951029Z","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","venue":null,"work_id":"4d77817e-2f4d-4368-8b2d-77f923702875","year":2020},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.645089Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:a1f46a99bcaed53ba287cfb1af1c3cd13626f2a254a83f8cbd2c3f6e06400e3b","observation_id":"d12a09f8-01ec-4b9b-a8cf-fc581688d909","resolution":{"observed_at":"2026-08-10T16:25:35.956760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.934703Z","title":"Learnable boundary guided adversarial training","venue":null,"work_id":"c747d5e8-d3cd-4c2e-8302-c53b272ec27d","year":2021},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.650113Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:d04a81ed44e689a96f06b323fa22295b43a0d93f05fb2868c2e735a34571283b","observation_id":"212eb2a6-28ae-482d-a738-7f64fc244fee","resolution":{"observed_at":"2026-08-10T16:25:35.940136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.916775Z","title":"Classes are not equal: An empirical study on image recognition fairness","venue":null,"work_id":"990c7f46-8836-4062-9b1f-cf2090a1f963","year":2024},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.655091Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:3f74c95e199d14fe932a2b98ce18430f7509eceb242aeb7e6bce20ba531dc113","observation_id":"9e59ff76-a46e-4304-b2f1-766368e4b86d","resolution":{"observed_at":"2026-08-10T16:25:35.922519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:34.660489Z","title":"Class-balanced loss based on effective number of samples","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.660489Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:83acd0757a6d07888da509c54ee3f9b58267210f316e3a1a4d04371f8fc7ecfc","observation_id":"321dfd4e-8a51-4b2d-819b-0af7431ac7cc","resolution":{"observed_at":"2026-08-10T16:25:34.660489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.11008","last_updated":"2017-10-19T03:39:56Z","snapshot_observed_at":"2026-08-14T21:08:53.042410Z","submitted_at":"2017-03-31T17:56:41Z","title":"Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.11008","snapshot_observed_at":"2026-08-10T16:25:34.665459Z","title":"Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.665459Z"},"links":{"cited_paper":"/paper/1703.11008","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:a99f27ef24c0ff562962492a3a4dbd655b3d49c707076822128bb1f2931b4b8e","observation_id":"ab9faaba-0dd6-4eaf-bcc3-90418a679990","resolution":{"observed_at":"2026-08-10T16:25:34.665459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.10272","last_updated":"2018-11-23T19:07:57Z","snapshot_observed_at":"2026-08-14T18:47:20.839941Z","submitted_at":"2018-07-26T17:58:26Z","title":"Evaluating and Understanding the Robustness of Adversarial Logit Pairing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.10272","snapshot_observed_at":"2026-08-10T16:25:34.671210Z","title":"Evaluating and understanding the robustness of adversarial logit pairing","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.671210Z"},"links":{"cited_paper":"/paper/1807.10272","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:56598df651a4effb4a80ef1cfe276cd16ffb393e1a83b7bf5f1b774e206a9ac2","observation_id":"743b55c2-0a6b-4795-9cda-6edb0cace20d","resolution":{"observed_at":"2026-08-10T16:25:34.671210Z","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":"2026-08-10T16:25:35.887521Z","title":"Generalizable adversarial training via spectral normalization","venue":null,"work_id":"4ac5e0ca-b432-4379-b091-55acfa4efa50","year":2019},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.676346Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:7c7658225067e9489cadb9b00f797b7fda49a9d705b9e0138ee3a295a5546425","observation_id":"e15145ff-4b7e-4e5c-8b62-6e3b4c35ea96","resolution":{"observed_at":"2026-08-10T16:25:35.893595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.865061Z","title":"\\\"U ber matrizen aus nicht negativen elementen","venue":null,"work_id":"a8379384-b929-4f63-8d6c-c9175ed60883","year":1912},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.681275Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:b0194f9f4cd95556169178d349d8c4cebee0acd028796fc7460b89a4efdff3d9","observation_id":"32924591-71e4-4d35-8cfd-da7925e7f916","resolution":{"observed_at":"2026-08-10T16:25:35.872812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.08329","last_updated":"2015-07-28T20:08:16Z","snapshot_observed_at":"2026-08-14T22:54:33.742381Z","submitted_at":"2015-03-28T17:19:49Z","title":"Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.08329","snapshot_observed_at":"2026-08-10T16:25:34.687352Z","title":"Risk bounds for the majority vote: From a pac-bayesian analysis to a learning algorithm","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.687352Z"},"links":{"cited_paper":"/paper/1503.08329","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:d1c557f49f20aa88db1e183828ff2ff5d7d40c46212cff16873b7f668df13d89","observation_id":"498c28e1-6017-4505-b3ce-11ecd0e7ddb1","resolution":{"observed_at":"2026-08-10T16:25:34.687352Z","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":"2026-08-10T16:25:34.695047Z","title":"Goodfellow, Jonathon Shlens, and Christian Szegedy","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.695047Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:10857145921c50bb4cd8fec9c79605957eda4d9c309e0bbf66beaa7e94247081","observation_id":"0d89e677-cf24-4b31-b1d7-4ed1523e6e31","resolution":{"observed_at":"2026-08-10T16:25:34.695047Z","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":"2026-08-10T16:25:35.835689Z","title":"Fairness without demographics in repeated loss minimization","venue":null,"work_id":"8098849a-5721-4dd0-8c0a-7ea6628eef24","year":2018},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.700589Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:fe31462975c03d67402409be8ffac5ecce348f00f07ebcb99b5e7c962d56ca9d","observation_id":"3cec6009-c24a-43bc-acb0-5da9edfa9ebb","resolution":{"observed_at":"2026-08-10T16:25:35.841596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:34.706933Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.706933Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:5fbc71d758fbebaf9e3cd0aa45ac1b4dd9990f6598293e71842f9129b93bf1e2","observation_id":"446e1729-e5f8-4453-92e8-7b714f139c5a","resolution":{"observed_at":"2026-08-10T16:25:34.706933Z","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":"2026-08-10T16:25:34.712139Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.712139Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:ee989012bd621f331d0c54dce557111f75dbe06fc7b8c7fe603a191f147e4e11","observation_id":"f9856278-a018-4ba4-831e-dd61231b157b","resolution":{"observed_at":"2026-08-10T16:25:34.712139Z","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":"2026-08-10T16:25:35.797934Z","title":"On generalization of graph autoencoders with adversarial training","venue":null,"work_id":"b4ab93ec-61dc-4e88-8052-a30b426c83d8","year":2021},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.716660Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:8f7462d66236647fc766d3ff690c3fda710653ec47b261e288bff1d17a68df46","observation_id":"5357b7c2-470e-4faa-843c-3b0d84973584","resolution":{"observed_at":"2026-08-10T16:25:35.803550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.781105Z","title":"Enhancing adversarial training via reweighting optimization trajectory","venue":null,"work_id":"b7d1d4d4-a2f4-4921-bab9-2da5d75e4402","year":2023},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.721593Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:0c2a6b20f1f0af19604c39a6ff370fc4eeb4192c4db8c4a961924bd4c3341ad4","observation_id":"a19f622d-7c50-45e2-b096-adcd3525080a","resolution":{"observed_at":"2026-08-10T16:25:35.786361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.765366Z","title":"Fantastic generalization measures and where to find them","venue":null,"work_id":"b9c4aae3-8148-4ad3-8d38-8b0503c22358","year":2020},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.726688Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:a64896d281af03b0b345c4f978e3c4f0533328c255bcabfdca81f729874d9852","observation_id":"e88138b1-5a54-4b6d-a389-bb22fa03f7f5","resolution":{"observed_at":"2026-08-10T16:25:35.770219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.749531Z","title":"How does weight correlation affect the generalisation ability of deep neural networks","venue":null,"work_id":"6c796eee-2c50-4e81-89c6-4d668937af35","year":2020},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.731599Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:e01c11ce11e7b13ffed3ba36e2ce7c37546766dd6a6282c89b098c3c60d73838","observation_id":"18630f4c-ec97-491e-a9d0-0f9599d82ad2","resolution":{"observed_at":"2026-08-10T16:25:35.754353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.734583Z","title":"Enhancing adversarial training with second-order statistics of weights","venue":null,"work_id":"327ae077-dc3d-4f17-9d1c-bf3b256e50d0","year":2022},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.736291Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:96d68a20c53d145ba6ac320ba1481c07939c964ea390ab097e1a510b6d6dc064","observation_id":"a09ccd36-408e-49ce-aeac-6a8b078d888b","resolution":{"observed_at":"2026-08-10T16:25:35.739134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.09209","last_updated":"2022-09-12T16:40:20Z","snapshot_observed_at":"2026-08-17T00:37:34.950668Z","submitted_at":"2022-01-23T09:03:22Z","title":"Weight Expansion: A New Perspective on Dropout and Generalization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.09209","snapshot_observed_at":"2026-08-10T16:25:34.741881Z","title":"Weight expansion: A new perspective on dropout and generalization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.741881Z"},"links":{"cited_paper":"/paper/2201.09209","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:277e72ba904878255baa6fb540c18e2938604a464eb129d011b672b0e60190a5","observation_id":"256a6d30-5df5-421a-ba7a-e8088d92286a","resolution":{"observed_at":"2026-08-10T16:25:34.741881Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.06373","last_updated":"2018-03-16T19:03:45Z","snapshot_observed_at":"2026-08-14T19:35:20.044888Z","submitted_at":"2018-03-16T19:03:45Z","title":"Adversarial Logit Pairing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.06373","snapshot_observed_at":"2026-08-10T16:25:34.747574Z","title":"Adversarial logit pairing","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.747574Z"},"links":{"cited_paper":"/paper/1803.06373","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:35a8fec27c58ae5ad702e8fc18af626d10ebffc58b65d9371a736492e46782f3","observation_id":"1dc761e0-6f92-4799-9658-efbdbd06704b","resolution":{"observed_at":"2026-08-10T16:25:34.747574Z","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":"2026-08-10T16:25:35.720002Z","title":"Pac-bayes bounds for the risk of the majority vote and the variance of the gibbs classifier","venue":null,"work_id":"bec691ff-ed09-4041-b2af-ccf8ee421e5e","year":2006},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.752758Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:b89900c921eca820f9a2a769b22ee71405719343a50ff80ebd1aa45d4fbfe7e1","observation_id":"2fb81c4b-c2db-4976-aea3-379b67eaa6b5","resolution":{"observed_at":"2026-08-10T16:25:35.724564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.701859Z","title":"(not) bounding the true error","venue":null,"work_id":"9e3dfccf-67df-498f-a015-01d662062268","year":2002},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.757975Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:b025c839e183127f524d003120676a8f7f579c2a43f5bfcd5ceb3750414db6a0","observation_id":"ed40a985-dcbc-4b79-aa16-0eabf5e11223","resolution":{"observed_at":"2026-08-10T16:25:35.707680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.683731Z","title":"Pac-bayes risk bounds for sample-compressed gibbs classifiers","venue":null,"work_id":"e5fb14aa-687c-4980-9830-d6d983936284","year":2005},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.763403Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:a1fdc202f1040f304f357817c165427e0f3bbaeb222d7640916e7229de0de154","observation_id":"d3183fe5-0f42-4fea-976f-fefce98e30f8","resolution":{"observed_at":"2026-08-10T16:25:35.689394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.667414Z","title":"Adversarial vertex mixup: Toward better adversarially robust generalization","venue":null,"work_id":"4db58111-a4c0-4c0b-972d-6070622072a0","year":2020},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.768328Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:e01b69e7febb8b40f5405d3267b38e112742995cba180d20b78e562783b25189","observation_id":"e0d0c3a9-e6d2-46a5-9da6-870a450c7434","resolution":{"observed_at":"2026-08-10T16:25:35.672942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.649529Z","title":"Wat: improve the worst-class robustness in adversarial training","venue":null,"work_id":"f71aba5d-5279-4b3e-81ac-462ab9aaa236","year":2023},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.773371Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:37b122674632f670d21f400fa686796f8421b634aadb5d8ca1e12a4cfffb02eb","observation_id":"211f5f2b-6316-46c3-8d2f-868ba5dc31e2","resolution":{"observed_at":"2026-08-10T16:25:35.655031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.633569Z","title":"Out-of-bounding-box triggers: A stealthy approach to cheat object detectors","venue":null,"work_id":"581f41e9-ac93-4de2-b686-f7174f31e937","year":2024},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.779127Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:f2635c6a6c93a6b2039b1f59ac13d87e08d34999b71507e28decae63996d5f2e","observation_id":"61926d2a-2de0-4c78-a520-2824b634e28f","resolution":{"observed_at":"2026-08-10T16:25:35.638362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.618194Z","title":"Just train twice: Improving group robustness without training group information","venue":null,"work_id":"5c90d091-7b29-48f3-9fd1-8db4c0ab4978","year":2021},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.783472Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:af865929b18dcb96c82ec560ddbe6dd404c8d544f865d9c7aaa3b13c477866af","observation_id":"edf67a6e-fdcb-4579-9de4-4ec2b311a5ea","resolution":{"observed_at":"2026-08-10T16:25:35.622913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:34.788034Z","title":"Large-scale long-tailed recognition in an open world","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.788034Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:ec9cf3c4d665f4d024bd77b19ec86771dc825112590d0ad79e3d9106000ef166","observation_id":"d8f388af-51c0-47c4-869c-d697c5c91b7a","resolution":{"observed_at":"2026-08-10T16:25:34.788034Z","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":"2026-08-10T16:25:35.592382Z","title":"On the tradeoff between robustness and fairness","venue":null,"work_id":"08226a51-4449-4f55-9dfd-d7c62563fde5","year":2022},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.792434Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:af0a0bf2cf65fedac397bfcabe4b497a6fd998c1735ce88d92e107013594bbd4","observation_id":"b084044f-0a08-4a71-9279-5778ac12e1bf","resolution":{"observed_at":"2026-08-10T16:25:35.596974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:34.797270Z","title":"Towards deep learning models resistant to adversarial attacks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.797270Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:eb240c542324c6bbe1f819b869ca680f6cf97c6618ae8c9f8aea69c705d69822","observation_id":"10e690d2-b3a9-4977-b066-2d7737a37d16","resolution":{"observed_at":"2026-08-10T16:25:34.797270Z","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":"2026-08-10T16:25:35.564560Z","title":"Simplified pac-bayesian margin bounds","venue":null,"work_id":"5b2faa8b-b206-4c83-94c2-b9cf80add265","year":2003},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.802024Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:955121ffc9b186f928a06b244cbe547c37adaca9baa43e33154d66edc73089fe","observation_id":"7aeb39f0-8a51-45ce-9c4a-a1e2e8d783a9","resolution":{"observed_at":"2026-08-10T16:25:35.569445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.549323Z","title":"Pac-bayesian model averaging","venue":null,"work_id":"9fbbddde-7945-47c1-ad22-3aa2815c3861","year":1999},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.806885Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:82287bdc12a570f46fa68055b3a6f639502df23a689f4adf01c0d36f6f6c09b4","observation_id":"ca4624eb-53f3-47f9-9b08-ced81cbc374d","resolution":{"observed_at":"2026-08-10T16:25:35.554327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:34.812212Z","title":"Long-tail learning via logit adjustment","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.812212Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:40935c13513fdcf989fa169260e790fddcddb30a9a67669ce92789ad26ef4331","observation_id":"a39b2c01-e302-4341-96ef-2e9477672cc0","resolution":{"observed_at":"2026-08-10T16:25:34.812212Z","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":"2026-08-10T16:25:35.520893Z","title":"Pac-bayesian generalization bound on confusion matrix for multi-class classification","venue":null,"work_id":"62bd5872-95f2-4d2e-873a-bc340cc730ef","year":2012},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.818468Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:1809a9edf2c643ed36906525304c09756580bc3461437e50cc2ace3f8dc7991a","observation_id":"76bd884f-5977-4e0e-84b3-fe9ddc88ec03","resolution":{"observed_at":"2026-08-10T16:25:35.526057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.502910Z","title":"Learning from failure: De-biasing classifier from biased classifier","venue":null,"work_id":"bba117f6-0448-4654-ad61-12ca66a0faef","year":2020},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.823742Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:1fa0edb5f7dd257a7df4dc82c1dd99b41ce0c146202cfeb11d060dcd6e3a9f53","observation_id":"1ae204df-6ca3-4b33-be7e-871f0366f267","resolution":{"observed_at":"2026-08-10T16:25:35.509592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.08947","last_updated":"2017-07-06T17:10:40Z","snapshot_observed_at":"2026-08-14T20:51:17.765084Z","submitted_at":"2017-06-27T17:20:06Z","title":"Exploring Generalization in Deep Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.08947","snapshot_observed_at":"2026-08-10T16:25:34.829358Z","title":"Exploring generalization in deep learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.829358Z"},"links":{"cited_paper":"/paper/1706.08947","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:92dc3d4261f43c97250a054c6a9f7c887eb3078a14e480dfc7116d96bb26d967","observation_id":"80f983a9-90f4-4be5-b653-8941537bcdaa","resolution":{"observed_at":"2026-08-10T16:25:34.829358Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.09564","last_updated":"2018-02-23T22:30:45Z","snapshot_observed_at":"2026-08-14T20:44:26.541416Z","submitted_at":"2017-07-29T22:36:35Z","title":"A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.09564","snapshot_observed_at":"2026-08-10T16:25:34.834734Z","title":"A pac-bayesian approach to spectrally-normalized margin bounds for neural networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.834734Z"},"links":{"cited_paper":"/paper/1707.09564","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:01840ac641c71727fdde391e8873a7615a430c1e8508a25d932d83d5fd1e9a8b","observation_id":"714a9ffd-92bf-403f-9952-e49e1233ef36","resolution":{"observed_at":"2026-08-10T16:25:34.834734Z","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":"2026-08-10T16:25:35.486425Z","title":"Robustness and accuracy could be reconcilable by (proper) definition","venue":null,"work_id":"5a655be1-a13f-43d3-87ab-e0d883732457","year":2022},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.841224Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:af8b78ca8db0554911d8dc46b02208b5abc28927c95cfab249ae2e28d25a8d5f","observation_id":"d7f61d20-5f01-41e5-bbfd-f8000e832b54","resolution":{"observed_at":"2026-08-10T16:25:35.491911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.466276Z","title":"Improving robust fariness via balance adversarial training","venue":null,"work_id":"e9cfd965-6958-47ea-a9db-a035071bb98b","year":2023},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.849808Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:6497cf6824b92e46235c17ee41fa276051bea2a51c9225986d86bed1f04537ed","observation_id":"922dc05f-308e-4577-9416-e4199e43a152","resolution":{"observed_at":"2026-08-10T16:25:35.472064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6199","last_updated":"2014-02-19T16:33:14Z","snapshot_observed_at":"2026-08-15T16:41:15.505782Z","submitted_at":"2013-12-21T03:36:08Z","title":"Intriguing properties of neural networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6199","snapshot_observed_at":"2026-08-10T16:25:34.857008Z","title":"Intriguing properties of neural networks","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.857008Z"},"links":{"cited_paper":"/paper/1312.6199","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:33c45978ca00b41cd4962ac5e55497fa39541eb060971466c9b369ecb876d606","observation_id":"722f8560-0cbf-42b0-893d-22c590541824","resolution":{"observed_at":"2026-08-10T16:25:34.857008Z","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":"2026-08-10T16:25:35.447245Z","title":"Better diffusion models further improve adversarial training","venue":null,"work_id":"8743a768-fb99-4f10-83a1-169b79274956","year":2023},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.863547Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:e95ae7408ec23c327f3a6da35e9535b1c6917d7f6fa8ba1522331fabf68e91eb","observation_id":"01cf5bff-4fcf-48e6-8299-6afa999a1a37","resolution":{"observed_at":"2026-08-10T16:25:35.452332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.430324Z","title":"Cfa: Class-wise calibrated fair adversarial training","venue":null,"work_id":"9b60737e-93e6-4fa1-893c-d226410573e7","year":2023},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.869388Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:3c42ea11e449a6da7463fe9f6fc117d1bbc9b320148889f6527d234d837dde03","observation_id":"fc3a6877-c8d6-4f4d-9339-43682fd68cda","resolution":{"observed_at":"2026-08-10T16:25:35.435131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.05990","last_updated":"2020-02-14T12:09:21Z","snapshot_observed_at":"2026-08-15T13:03:00.221180Z","submitted_at":"2020-02-14T12:09:21Z","title":"Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.05990","snapshot_observed_at":"2026-08-10T16:25:34.875327Z","title":"Skip connections matter: On the transferability of adversarial examples generated with resnets","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.875327Z"},"links":{"cited_paper":"/paper/2002.05990","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:a19b14dca6b0c23b8412af3d8302763846436b20ec0ec19188568d77bf23ce87","observation_id":"13a46f95-c563-4d0e-b949-e34e069d2c45","resolution":{"observed_at":"2026-08-10T16:25:34.875327Z","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":"2026-08-10T16:25:35.413581Z","title":"Adversarial weight perturbation helps robust generalization","venue":null,"work_id":"b19e73e0-8c7b-48df-bec7-3f1cd29ca03d","year":2020},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.883028Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:390284fb34b45268e2ed6cf955b18dee7bb631de57e3dcbfba3672381d8d161e","observation_id":"17426796-0fc5-40c9-8143-c73348751ea0","resolution":{"observed_at":"2026-08-10T16:25:35.419620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.395799Z","title":"Pac-bayesian spectrally-normalized bounds for adversarially robust generalization","venue":null,"work_id":"4dc61856-ede1-40c8-9016-77d46eb399c7","year":2023},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.888822Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:e0b7dfdb54caa79b9b125ba2c0eee275eb2f4b5508097dc0ad2855e6d7c3b91f","observation_id":"ec79fd4a-8119-455b-8cc7-003f649865b6","resolution":{"observed_at":"2026-08-10T16:25:35.401689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.378738Z","title":"To be robust or to be fair: Towards fairness in adversarial training","venue":null,"work_id":"d73f6f93-b9f4-4799-827b-51982acb9302","year":2021},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.893833Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:2987e725d133f0bebf2576e85a173d8ccc083c16af4f85064a77d3edb5b333cd","observation_id":"7487a7fc-01db-48f1-87a3-b52df551ccce","resolution":{"observed_at":"2026-08-10T16:25:35.384102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.10941","last_updated":"2017-05-31T04:56:25Z","snapshot_observed_at":"2026-08-18T07:03:56.924363Z","submitted_at":"2017-05-31T04:56:25Z","title":"Spectral Norm Regularization for Improving the Generalizability of Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.10941","snapshot_observed_at":"2026-08-10T16:25:34.899640Z","title":"Spectral norm regularization for improving the generalizability of deep learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.899640Z"},"links":{"cited_paper":"/paper/1705.10941","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:ded57698c295fa06e9b6127f46278d23d69f7b14b3de29c4312fbac1fbd9f7ff","observation_id":"2b8cb38a-9d23-4e81-a4eb-c635edcb59a9","resolution":{"observed_at":"2026-08-10T16:25:34.899640Z","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":"2026-08-10T16:25:34.905907Z","title":"Wide residual networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.905907Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:9b6a3522532bf0996aa2ce6aa39923cfa8c56008c379a90e5e266ecf45314a7a","observation_id":"15b4b547-77a9-4532-9671-7a120b20a184","resolution":{"observed_at":"2026-08-10T16:25:34.905907Z","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":"2026-08-10T16:25:35.348748Z","title":"Theoretically principled trade-off between robustness and accuracy","venue":null,"work_id":"c8f41d12-d4a0-4885-8d8a-acad93f5e52c","year":2019},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.911183Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:ef9d3b1fd9b568fe7414fc2ffa1dea6c95eda9a870e9519b1fcba96f909b8108","observation_id":"7fdad96a-b7ea-4e20-8978-3e464d455335","resolution":{"observed_at":"2026-08-10T16:25:35.354652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:25:35.329411Z","title":"Trajpac: Towards robustness verification of pedestrian trajectory prediction models","venue":null,"work_id":"5a19ac10-30f4-45ac-b6fb-299842e4f52b","year":2023},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.916010Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:3b047c880409c6edd0f06e4615146b8447e8ff8a2eb46f558b7e92cf9253890a","observation_id":"0e63b80f-ade1-4e0b-82de-db6e5c830ed6","resolution":{"observed_at":"2026-08-10T16:25:35.336368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04819","last_updated":"2021-03-17T19:43:43Z","snapshot_observed_at":"2026-08-17T21:16:40.136584Z","submitted_at":"2020-10-09T21:38:14Z","title":"How Does Mixup Help With Robustness and Generalization?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.04819","snapshot_observed_at":"2026-08-10T16:25:34.922832Z","title":"How does mixup help with robustness and generalization? arXiv preprint arXiv:2010.04819, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.922832Z"},"links":{"cited_paper":"/paper/2010.04819","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:1d03bb1e9a6b339d783148b45d920aabae009b10c210f7c542b961db823492a0","observation_id":"43cfcc74-d8ad-4f91-893d-8eefb1ac52ae","resolution":{"observed_at":"2026-08-10T16:25:34.922832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17729","last_updated":"2024-03-27T23:33:15Z","snapshot_observed_at":"2026-08-16T14:14:48.040843Z","submitted_at":"2024-02-27T18:01:59Z","title":"Towards Fairness-Aware Adversarial Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17729","snapshot_observed_at":"2026-08-10T16:25:34.928503Z","title":"Towards fairness-aware adversarial learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.928503Z"},"links":{"cited_paper":"/paper/2402.17729","citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:80370fd6261758ba7774d572f97c41e8d3faede3e6cfe07f515d0c8a59b0ade4","observation_id":"4870a559-5c7d-44a7-9341-4b9a26757620","resolution":{"observed_at":"2026-08-10T16:25:34.928503Z","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":"2026-08-10T16:25:34.936476Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.936476Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:42d7886345002343ac443ae6de9d3ab0b77b4148021155c08c4b87eec63ba61b","observation_id":"35a5273e-fe5f-4d68-a7c9-db75e9d37004","resolution":{"observed_at":"2026-08-10T16:25:34.936476Z","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":"2026-08-10T16:25:34.951631Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.951631Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:89352e40eaba7a317780d69a51cee5af0470e53cfb411d8c30cac92d587c5239","observation_id":"bfa052a2-8c04-4758-9f5f-8ac28c927dd0","resolution":{"observed_at":"2026-08-10T16:25:34.951631Z","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":"2026-08-10T16:25:34.967405Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.967405Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:c4fb821ac87a2dccf625fa0519f1f0bca4c5d07f457bd69dc160e20da60cf502","observation_id":"8a581eed-265f-4fb3-800b-c91ccca20478","resolution":{"observed_at":"2026-08-10T16:25:34.967405Z","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":"2026-08-10T16:25:34.975992Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-10T16:25:34.975992Z"},"links":{"citing_paper":"/paper/2501.13273"},"observation_digest":"sha256:d4cd2caa3cb01a08ef4c5e5e60b41b39773ff7b9222554d4ec12dd2368051417","observation_id":"e28ac423-9c5f-4be6-8a50-cba403b057fb","resolution":{"observed_at":"2026-08-10T16:25:34.975992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.13273","last_updated":"2025-01-22T23:32:19Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T22:22:23.435742Z","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization"},"reference_resolution":{"displayed":64,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":27,"verified_exact":0,"verified_fuzzy":37},"total_outbound_references":64},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2501.13273."}