{"as_of":"2026-08-19T05:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6506278991d4df0c49f1a934f53cd9bd461ff8fb6a69e2d38ae3bee484b0050c","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":31,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:14:38.826853Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":346,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2005.01643","last_updated":"2020-11-01T23:50:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-04T17:00:15Z","title":"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-11T11:33:20.892688Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2005.01643"},"observation_digest":"sha256:df5870045210bf819b6a751cb8943e7486281769ad0867cf4d7a36e67d9fa50c","observation_id":"2dd2e29e-31f7-4bdf-90b2-5979171ffdb5","resolution":{"observed_at":"2026-05-11T11:33:21.220075Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-08-13T15:53:46.844813Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":122,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:dc23a10865ea0c692de0e9f80a5453628a3c435c06493debfe80406ac9f968bb","observation_id":"57d40b0e-3ef7-499f-ae08-f6120b8508bd","resolution":{"observed_at":"2026-05-24T02:43:47.628269Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-11T10:42:49.299692Z","title":"Certifying some distributional robustness with princi- pled adversarial training","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16361","last_updated":"2024-12-20T21:49:02Z","snapshot_observed_at":"2026-08-15T17:51:01.897146Z","submitted_at":"2024-12-20T21:49:02Z","title":"Toward Robust Neural Reconstruction from Sparse Point Sets","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T10:42:49.299692Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2412.16361"},"observation_digest":"sha256:aba033ee49553785137cbab37c3f558c13abba084722bde3160ae070738bf52e","observation_id":"1f5af60f-0547-42fa-9fe7-0ca404aa3417","resolution":{"observed_at":"2026-08-11T10:42:49.299692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-10T23:26:24.789734Z","title":"Certifying some distributional robustness with principled adversarial training","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.20556","last_updated":"2026-07-15T21:51:08Z","snapshot_observed_at":"2026-08-11T06:06:08.236600Z","submitted_at":"2024-12-29T19:31:23Z","title":"Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T23:26:24.789734Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2412.20556"},"observation_digest":"sha256:643e87a9747498e2eb40f964fd54aa482ce382bc21df72eb95cd70077276bde7","observation_id":"5cbfd8a8-6548-4c24-8123-5cbb01d1afa2","resolution":{"observed_at":"2026-08-10T23:26:24.789734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-15T21:14:38.826853Z","title":"S INHA , H","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.10631","last_updated":"2025-05-15T18:12:31Z","snapshot_observed_at":"2026-08-15T21:04:07.971416Z","submitted_at":"2025-05-15T18:12:31Z","title":"Decentralized Min-Max Optimization with Gradient Tracking","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T21:14:38.826853Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2505.10631"},"observation_digest":"sha256:a51e84ec5eec2df440a07c2e4b9d6344defa74602f09a46c0bf3cb8c6a82c7d9","observation_id":"e1e1a63e-05cb-461e-8705-83e75ce0d890","resolution":{"observed_at":"2026-08-15T21:14:38.826853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-07T13:40:21.948421Z","title":"Certifying some distributional robustness with principled adversarial training.arXiv preprint arXiv:1710.10571, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.21422","last_updated":"2025-05-27T16:50:44Z","snapshot_observed_at":"2026-08-14T13:25:58.224441Z","submitted_at":"2025-05-27T16:50:44Z","title":"When Shift Happens - Confounding Is to Blame","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T13:40:21.948421Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2505.21422"},"observation_digest":"sha256:4552ba27f5c8ed55bb25c3edde9b0042955d42160228f192fa8e753e74009020","observation_id":"bad7c10f-9333-457f-928a-75592f9ee03f","resolution":{"observed_at":"2026-08-07T13:40:21.948421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-06T23:51:00.164072Z","title":"Certifying some distributional robustness with principled adversarial training","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.16218","last_updated":"2025-07-30T16:57:19Z","snapshot_observed_at":"2026-08-18T17:32:31.575977Z","submitted_at":"2025-06-19T11:16:02Z","title":"FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models","version":3},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-06T23:51:00.164072Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2506.16218"},"observation_digest":"sha256:aba2776398b3c88e1eed07c5d09df338eb4e04bdcf28f370c79994a8dc48b8a6","observation_id":"872eb3c9-cdd6-46f7-824d-762c067ef757","resolution":{"observed_at":"2026-08-06T23:51:00.164072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-15T19:06:49.540461Z","title":"Certifiable distributional robustness with principled adversarial training","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.17874","last_updated":"2025-06-24T21:04:53Z","snapshot_observed_at":"2026-08-19T01:38:20.259461Z","submitted_at":"2025-06-22T02:18:03Z","title":"DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T19:06:49.540461Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2506.17874"},"observation_digest":"sha256:a5c5f830b8b4a484dbe0bffa0e197c027850f8c94acf8af25694fc1fab6f3a61","observation_id":"a6d40ee1-e43f-4869-882d-03fd8065c992","resolution":{"observed_at":"2026-08-15T19:06:49.540461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-06T21:38:26.029403Z","title":"Certifiable Distributional Robustness with Principled Adversarial Training,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.23703","last_updated":"2025-06-30T10:26:59Z","snapshot_observed_at":"2026-08-15T08:11:47.077789Z","submitted_at":"2025-06-30T10:26:59Z","title":"A New Perspective On AI Safety Through Control Theory Methodologies","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-06T21:38:26.029403Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2506.23703"},"observation_digest":"sha256:aebf3db482d9e6dfc777aff5e8f36304566f2e98ce94e1eb92568d10fee12409","observation_id":"2c7be32c-07bd-402c-9654-5997a4148884","resolution":{"observed_at":"2026-08-06T21:38:26.029403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2507.01932","last_updated":"2026-05-19T17:55:59Z","snapshot_observed_at":"2026-08-17T15:42:02.586029Z","submitted_at":"2025-07-02T17:45:10Z","title":"A first-order method for nonconvex-nonconcave minimax problems under a local Kurdyka-Lojasiewicz condition","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-21T23:35:03.424607Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2507.01932"},"observation_digest":"sha256:09039aa84d75c384c1fb19b998dc95b40cae2533504d7c52191169a71c886edd","observation_id":"b9336108-d6a0-4497-964b-c67a176e6daf","resolution":{"observed_at":"2026-05-21T23:35:45.714678Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-06T20:22:28.803320Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.03175","last_updated":"2025-07-03T21:06:30Z","snapshot_observed_at":"2026-08-14T01:12:44.422203Z","submitted_at":"2025-07-03T21:06:30Z","title":"Understanding Knowledge Transferability for Transfer Learning: A Survey","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T20:22:28.803320Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2507.03175"},"observation_digest":"sha256:254b1364b11c3a2ef061ddc7634f00f50a0499f8f39f515dafdd0ba3d6dcd082","observation_id":"bc9e8de9-2419-4b17-b5d6-72137e5810ed","resolution":{"observed_at":"2026-08-06T20:22:28.803320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T14:28:34.612349Z","title":"Certifyin g some distributional robustness with principled adversarial tr aining,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.21431","last_updated":"2025-08-29T08:59:04Z","snapshot_observed_at":"2026-08-15T22:33:55.521140Z","submitted_at":"2025-08-29T08:59:04Z","title":"An Optimistic Gradient Tracking Method for Distributed Minimax Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T14:28:34.612349Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2508.21431"},"observation_digest":"sha256:e128de3c7ff7b1981879e7f0d9a3047f1850ccd9a6e7027fa3e605fa3aa262d6","observation_id":"dfdb64a4-401b-45da-b8db-89bf88fe795b","resolution":{"observed_at":"2026-08-05T14:28:34.612349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-04T20:03:07.691117Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.08942","last_updated":"2025-09-10T19:08:17Z","snapshot_observed_at":"2026-08-18T19:04:13.762521Z","submitted_at":"2025-09-10T19:08:17Z","title":"Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-04T20:03:07.691117Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2509.08942"},"observation_digest":"sha256:90bc2965132aa2fb04df9fffaffc2c10453c0895105cb61bf02f8f1a734ebe70","observation_id":"da2d90b1-b867-4696-adb8-e1a8bf70a2d2","resolution":{"observed_at":"2026-08-04T20:03:07.691117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-04T17:30:02.248572Z","title":", author Namkoong, H","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10913","last_updated":"2025-09-13T17:27:37Z","snapshot_observed_at":"2026-08-17T22:36:38.304356Z","submitted_at":"2025-09-13T17:27:37Z","title":"Robustifying Diffusion-Denoised Smoothing Against Covariate Shift","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-04T17:30:02.248572Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2509.10913"},"observation_digest":"sha256:6312c9c82d29557e71845f818d9aa06573903be978c7a6ba6d2d88fa38a4bdbc","observation_id":"66a2b13d-0413-4cd4-ad3c-169aa5fbb748","resolution":{"observed_at":"2026-08-04T17:30:02.248572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-04T13:54:24.304742Z","title":"Certifying some distributional robustness with principled adversarial training","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.24894","last_updated":"2026-06-18T13:27:17Z","snapshot_observed_at":"2026-08-16T18:21:23.067659Z","submitted_at":"2025-09-29T15:03:55Z","title":"Improved Stochastic Optimization of LogSumExp","version":4},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-04T13:54:24.304742Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2509.24894"},"observation_digest":"sha256:318d65c0154221e2521a598ffe32b05316579cac6911c51d08cc5776c4863f1a","observation_id":"48bf6cd9-bfd6-4f8b-b027-1c58c23ef108","resolution":{"observed_at":"2026-08-04T13:54:24.304742Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-04T13:26:09.882829Z","title":"Sinha, H","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.01168","last_updated":"2026-05-26T16:22:06Z","snapshot_observed_at":"2026-08-17T15:42:48.007839Z","submitted_at":"2025-10-01T17:54:27Z","title":"A first-order method for constrained nonconvex-nonconcave minimax optimization","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T13:26:09.882829Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2510.01168"},"observation_digest":"sha256:05087e37d2edf92189bc94829733a09abf235597a1cc534b221505a2d1d6cf55","observation_id":"1fc03eca-7269-429b-920c-8873d4713db3","resolution":{"observed_at":"2026-08-04T13:26:09.882829Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-04T07:29:40.934064Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.25956","last_updated":"2026-05-25T19:59:22Z","snapshot_observed_at":"2026-08-10T03:39:50.843757Z","submitted_at":"2025-10-29T20:53:44Z","title":"Gradient Flow Sampler-based Distributionally Robust Optimization","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-04T07:29:40.934064Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2510.25956"},"observation_digest":"sha256:1249e65456b5c5c23b2cf09c722001efa5b53cae7ee207260c99448d31666994","observation_id":"238f0d35-122d-4d9d-8393-7d993427be90","resolution":{"observed_at":"2026-08-04T07:29:40.934064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2602.00844","last_updated":"2026-05-06T01:11:30Z","snapshot_observed_at":"2026-08-13T05:34:07.008716Z","submitted_at":"2026-01-31T18:15:03Z","title":"Multivariate Time Series Data Imputation via Distributionally Robust Regularization","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-16T08:42:34.961550Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2602.00844"},"observation_digest":"sha256:d7a5e611739dafb24ea9c147862be6dd253c3270118ed0d85f5a5ba9615174e0","observation_id":"5f7da2bc-5e21-494b-a307-44d7d38664c6","resolution":{"observed_at":"2026-05-16T08:42:36.972496Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-02T22:19:23.064805Z","title":"Certifying some distributional robustness with principled adversarial training.arXiv preprint arXiv:1710.10571, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2602.17743","last_updated":"2026-07-20T14:54:26Z","snapshot_observed_at":"2026-08-17T18:19:58.738530Z","submitted_at":"2026-02-19T12:37:00Z","title":"Bigger Is Safer: Provable Robustness in In-Context Learning Scales with Capacity","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T22:19:23.064805Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2602.17743"},"observation_digest":"sha256:761885b30b883d093e145b23dffff2e7e50289e82f2dba8d508a3ae4888efcc6","observation_id":"46cc1d35-cf3f-4d3a-a030-5919ea97bc5e","resolution":{"observed_at":"2026-08-02T22:19:23.064805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2604.04342","last_updated":"2026-06-16T21:43:12Z","snapshot_observed_at":"2026-08-11T14:54:50.249572Z","submitted_at":"2026-04-06T01:35:13Z","title":"Generative models for decision-making under distributional shift","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T20:27:24.297535Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2604.04342"},"observation_digest":"sha256:6fcbe0daf07fb252ce924a2750c8a729013923d7d0d905ed73608eaaec919c68","observation_id":"7f09cdad-8c9b-42e2-9bb6-4d4def9fc6dc","resolution":{"observed_at":"2026-05-10T21:55:51.941632Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2604.08404","last_updated":"2026-04-20T18:01:41Z","snapshot_observed_at":"2026-08-15T00:35:58.068836Z","submitted_at":"2026-04-09T16:02:07Z","title":"Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T17:00:51.046061Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2604.08404"},"observation_digest":"sha256:d5565f452ae15278fb4b1d31cfb28b3fa039b440778c10666c76056c9dd9c880","observation_id":"a4fe4a40-a333-43b6-95cd-4f95f6936e6f","resolution":{"observed_at":"2026-05-11T07:41:01.655550Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2604.11507","last_updated":"2026-04-13T14:11:06Z","snapshot_observed_at":"2026-08-18T02:12:02.779856Z","submitted_at":"2026-04-13T14:11:06Z","title":"Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers","version":1},"reference_index":115,"source":"pdf_text","source_observed_at":"2026-05-10T15:40:10.971282Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2604.11507"},"observation_digest":"sha256:63b05012fee91b99b7f0252d067ca2992ba211c3c471e6c662cb295859b7ac59","observation_id":"ecbd4be2-08e8-44dd-8cb7-6de2d2195728","resolution":{"observed_at":"2026-05-10T15:40:33.097233Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2604.25848","last_updated":"2026-06-17T14:29:11Z","snapshot_observed_at":"2026-08-16T00:01:06.394472Z","submitted_at":"2026-04-28T16:54:03Z","title":"A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-07T16:14:02.445730Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2604.25848"},"observation_digest":"sha256:eca6d0de9fcb77e0dc1b1bcfab3b48f639601d881aeab30382aa67f51de4cd89","observation_id":"7cfe2c26-d859-48f0-b7bc-58005cff7edf","resolution":{"observed_at":"2026-05-09T01:59:36.018469Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2604.26128","last_updated":"2026-04-28T21:36:56Z","snapshot_observed_at":"2026-07-06T23:11:52.954360Z","submitted_at":"2026-04-28T21:36:56Z","title":"Robust Representation Learning through Explicit Environment Modeling","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-07T14:21:27.965959Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2604.26128"},"observation_digest":"sha256:c400bcd9f3434c7055be616fc31befc7669760a3850e6fc060ca416e19fa5180","observation_id":"ea387019-ba3d-4af7-9940-32e29672bc61","resolution":{"observed_at":"2026-05-12T00:46:12.925875Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2605.05660","last_updated":"2026-05-07T04:24:17Z","snapshot_observed_at":"2026-08-18T02:15:48.757455Z","submitted_at":"2026-05-07T04:24:17Z","title":"Distributionally Robust Multi-Objective Optimization","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-08T15:00:37.589354Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2605.05660"},"observation_digest":"sha256:8277ccb03a92d9df06759318a3c921590cab37a85bdd1c62ee59915ec5229d9f","observation_id":"94392920-11f3-44db-a3fa-76c2d38cab80","resolution":{"observed_at":"2026-05-11T18:36:08.406743Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2605.11170","last_updated":"2026-06-02T13:57:02Z","snapshot_observed_at":"2026-08-02T02:13:17.912919Z","submitted_at":"2026-05-11T19:28:33Z","title":"Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-13T05:56:38.042978Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2605.11170"},"observation_digest":"sha256:8b30ea80eea58931ac16d28519d6e60e7d21d82b42575262a8082ed1872700b3","observation_id":"813e46d4-2c4a-4386-b132-eea4f69e4d4b","resolution":{"observed_at":"2026-05-13T05:57:21.620183Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2605.23203","last_updated":"2026-05-22T03:37:34Z","snapshot_observed_at":"2026-08-15T00:31:02.461636Z","submitted_at":"2026-05-22T03:37:34Z","title":"Lipschitz Optimization for Formal Verification of Homographies","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-25T04:57:25.453030Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2605.23203"},"observation_digest":"sha256:395c1019dffd4682908ac77efc57b14a01635231c1febdacfc21eaa1bfea957e","observation_id":"ef9cc49c-c950-4b3b-b723-9ffa61335002","resolution":{"observed_at":"2026-05-25T05:00:22.240908Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2606.07003","last_updated":"2026-06-05T07:49:32Z","snapshot_observed_at":"2026-08-11T08:26:13.880980Z","submitted_at":"2026-06-05T07:49:32Z","title":"A Single-Loop Regularized Newton Method for Nonconvex-Strongly-Concave Minimax Optimization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-27T21:32:49.075190Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2606.07003"},"observation_digest":"sha256:6e83d469c17cda699cb041532d47bfe3357ea749cab2d58e2d6e1d1e759c01a2","observation_id":"af43a220-cfc3-431f-973b-79557b9af0f7","resolution":{"observed_at":"2026-07-02T19:17:18.495905Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2606.12251","last_updated":"2026-06-10T15:53:36Z","snapshot_observed_at":"2026-08-14T14:18:22.072790Z","submitted_at":"2026-06-10T15:53:36Z","title":"Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-27T10:17:01.777614Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2606.12251"},"observation_digest":"sha256:bec3bc065e778e401c151cdc5fd2d2548a1831a454bfee64890797b692e8f440","observation_id":"e05ba2fb-9953-4066-bfc6-96a436a34447","resolution":{"observed_at":"2026-07-03T09:57:56.243589Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":"1710.10571","doi":"10.48550/arxiv.1710.10571","metadata_source":"arxiv_reference","pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Certifying some distributional robustness with principled adversarial training","venue":"arXiv (Cornell University)","work_id":"f13c7954-0712-439e-8efc-0f9b560646d0","year":2017},"citing_paper":{"arxiv_id":"2606.12655","last_updated":"2026-06-10T20:27:06Z","snapshot_observed_at":"2026-08-19T04:16:30.104985Z","submitted_at":"2026-06-10T20:27:06Z","title":"Amnesia: A Stealthy Replay Attack on Continual Learning Dreams","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-06-27T09:02:44.548488Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2606.12655"},"observation_digest":"sha256:fcaef63a4cae9c655a1fac0cdb0e06e4566a5d17445396e87491c00a3c84e819","observation_id":"8837ec2b-5c38-42f5-ba1f-f1d5644ad90d","resolution":{"observed_at":"2026-07-03T12:18:06.665574Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10571","snapshot_observed_at":"2026-08-02T05:17:10.321266Z","title":"Certifying some distributional robustness with principled adversarial training,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.13436","last_updated":"2026-07-15T04:40:09Z","snapshot_observed_at":"2026-08-17T23:05:50.129148Z","submitted_at":"2026-07-15T04:40:09Z","title":"Distributionally Robust and Safe Imitation Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T05:17:10.321266Z"},"links":{"cited_paper":"/paper/1710.10571","citing_paper":"/paper/2607.13436"},"observation_digest":"sha256:6504d3c5b8e246b36e4195235ac330af38e3d14cc985c2801ab994a939284786","observation_id":"3f57ffe0-5991-48b9-b0a3-e48c1125bdc6","resolution":{"observed_at":"2026-08-02T05:17:10.321266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1710.10571/citation-record","integrity":"/paper/1710.10571/integrity","json":"/paper/1710.10571/citation-record.json","paper":"/paper/1710.10571"},"outbound":[],"paper":{"arxiv_id":"1710.10571","last_updated":"2020-05-01T07:29:34Z","latest_version":5,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-14T20:19:17.796762Z","submitted_at":"2017-10-29T07:27:57Z","title":"Certifying Some Distributional Robustness with Principled Adversarial Training"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:1710.10571."}