{"as_of":"2026-08-10T08:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4825e92c411701cedd8718e73e2a0d4f0500fafe2753bcba0e26f49ab93bfbdb","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T19:42:54.438936Z","state":"measured"},{"denominator":56,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":56,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2502.00279/citation-record","integrity":"/paper/2502.00279/integrity","json":"/paper/2502.00279/citation-record.json","paper":"/paper/2502.00279"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.049619Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.049619Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:ddab3885355f8ed603eba040e8f4ac9ed6bd51754df5acaf0252137d1c705f58","observation_id":"54acb57c-11cb-428d-adf2-af2fba758996","resolution":{"observed_at":"2026-08-09T19:42:54.049619Z","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":"2026-08-09T19:42:55.367202Z","title":"Maximum likelihood with bias-corrected calibration is hard-to-beat at label shift adaptation","venue":null,"work_id":"b5a80524-cd87-423e-838d-b64205587407","year":2020},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.056085Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:3e69ed938923322a700df05a177463ff76c31a7aa7fdf74386f7134ccbb5e000","observation_id":"bddfbc3e-c9ee-40a5-971e-625543d8548b","resolution":{"observed_at":"2026-08-09T19:42:55.372039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.352036Z","title":"E., and McGuinness, K","venue":null,"work_id":"f7e0d728-df0a-4987-9775-972f9afa6e77","year":2020},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.061458Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:fd764a85aef7e24c7847873aa3fbf7077ff3a0316d5339f425c2f6cf854f22f8","observation_id":"19ae7986-c167-474b-a5f8-4b89bf721652","resolution":{"observed_at":"2026-08-09T19:42:55.356436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.09734","last_updated":"2019-03-22T23:46:24Z","snapshot_observed_at":"2026-07-06T07:41:06.398227Z","submitted_at":"2019-03-22T23:46:24Z","title":"Regularized Learning for Domain Adaptation under Label Shifts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.09734","snapshot_observed_at":"2026-08-09T19:42:54.066532Z","title":"Regularized learning for domain adaptation under label shifts","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.066532Z"},"links":{"cited_paper":"/paper/1903.09734","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:9e858375aed2c72ba4a8d8d24b49aaf231563f9a05bfbd17de95b1383caec845","observation_id":"6165cc4e-dcf4-480b-9454-b7f09c9c054b","resolution":{"observed_at":"2026-08-09T19:42:54.066532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-09T11:55:02.726934Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-09T19:42:54.071853Z","title":"D., Kurakin, A., Sohn, K., Zhang, H., and Raffel, C","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.071853Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:8ebc431dd2f639b40821069b01dcf0cacf7458175bbf79c5f6359e5530a134a4","observation_id":"c4f662c4-f335-4fd6-aee4-65f8d4890af8","resolution":{"observed_at":"2026-08-09T19:42:54.071853Z","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":"2026-08-09T19:42:55.336139Z","title":null,"venue":null,"work_id":"0e9b1458-c0d3-4ef2-ac19-8ee2ead43346","year":2019},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.077421Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:32f878e1988a99f322602930226563f5aef9e992d6592b3c0b438d83338d18d2","observation_id":"0acfb2d6-336d-4476-9403-e57a8aeeeefe","resolution":{"observed_at":"2026-08-09T19:42:55.341289Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.319603Z","title":null,"venue":null,"work_id":"483fcc57-322e-4cf4-bc8b-ad783d80a738","year":2018},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.082609Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:0a3a2f8dc61914b14931a11f127c8e9425fd631dd80d2997be5ab1003a992e2c","observation_id":"16aa7b26-3bc4-429b-88d3-af288128f8f5","resolution":{"observed_at":"2026-08-09T19:42:55.324736Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.303285Z","title":"Semi-supervised learning (chapelle, o","venue":null,"work_id":"bb3c2193-5a99-418a-b02c-a9dfef479e60","year":2006},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.087863Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:6318481278c7c48a82aa323f5a1cf0882d91a93c178ca0e23f6bab2cb377efce","observation_id":"b54ac618-8fe7-494b-8503-2f4f8f2f9352","resolution":{"observed_at":"2026-08-09T19:42:55.308261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.092551Z","title":"Double/debiased machine learning for treatment and structural parameters, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.092551Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:cf548d7e055cb41f6e10cd7b87aba52e6655f0c10eba1c8c92fd8780b0f36d85","observation_id":"5180073c-e989-493b-8207-52873e62ff4f","resolution":{"observed_at":"2026-08-09T19:42:54.092551Z","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":"2026-08-09T19:42:55.276523Z","title":"M., and Syrgkanis, V","venue":null,"work_id":"177fbc4a-369d-4ffe-a8df-1e92f8fa13e1","year":2022},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.113032Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:c6554ee180bedba2022f00ccc35640d28d370831f11a513523b2919c3842310b","observation_id":"7d40dff4-4b93-4d95-ac7e-ed230f91555f","resolution":{"observed_at":"2026-08-09T19:42:55.281834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.260000Z","title":"An analysis of single-layer networks in unsupervised feature learning","venue":null,"work_id":"87e2ffe4-19b1-4008-a748-0ae34eebae30","year":2011},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.160819Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:cbe1e623ceb6ebf59ac4124d028a5cabd0d369f2f465e94cc50f2d44a3e83411","observation_id":"63907c7e-3772-4c2c-8633-2c8bde04058d","resolution":{"observed_at":"2026-08-09T19:42:55.265353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.199469Z","title":"Class-balanced loss based on effective number of samples","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.199469Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:cfe1170e680bb8bb3a4e1ed1941a2fe1940bf138ac3ac4087740664cd5542243","observation_id":"0a294753-78a0-4cce-9e39-a6ae8f7490d4","resolution":{"observed_at":"2026-08-09T19:42:54.199469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13505","last_updated":"2024-07-30T07:52:34Z","snapshot_observed_at":"2026-08-05T01:22:45.377898Z","submitted_at":"2024-02-21T03:39:04Z","title":"SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13505","snapshot_observed_at":"2026-08-09T19:42:54.204279Z","title":"Simpro: A simple probabilistic framework towards realistic long-tailed semi-supervised learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.204279Z"},"links":{"cited_paper":"/paper/2402.13505","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:b36951a1a8ea47a6a9262404af6d6d64e70282537836f6fee05280afa51b0eaf","observation_id":"e4559739-6730-41e8-a787-bf5de79a6753","resolution":{"observed_at":"2026-08-09T19:42:54.204279Z","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":"2026-08-09T19:42:55.233191Z","title":"Rda: Reciprocal distribution alignment for robust semi-supervised learning","venue":null,"work_id":"5e07fc19-d4a4-4cac-a465-dfcdbc2aab66","year":2022},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.209243Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:f4aff51f619c1f8943a3ad401da6f5bdd0c827bf0a785ffe0501fe9d88b57739","observation_id":"32013ef8-d86f-4c37-80e7-d7dc31c0ef78","resolution":{"observed_at":"2026-08-09T19:42:55.238471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.216534Z","title":"Towards semi-supervised learning with non-random missing labels","venue":null,"work_id":"487ccc75-ff06-423b-8625-1fc89a7e5d39","year":2023},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.213531Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:0a70c3879b24767cd7e93836dbeaa4ec8caf3343a73fc41f832d93f8da6810ec","observation_id":"b70d35fb-979c-4e21-a7fa-440fe581fc91","resolution":{"observed_at":"2026-08-09T19:42:55.222162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.199780Z","title":"Cossl: Co-learning of representation and classifier for imbalanced semi-supervised learning","venue":null,"work_id":"63d4bfba-391b-49d5-810b-9b568ccb2b40","year":2022},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.217833Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:cf5ce53aa63e7acffbff2beb675ddde14fd4ee0ac0d37f3533c63a9be5748ca4","observation_id":"d8a09d87-f01b-4925-9893-26c505130fa1","resolution":{"observed_at":"2026-08-09T19:42:55.204926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.222182Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.222182Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:b2a0a1349b7ae4a95b3e7ec52e0361a5e771b6181c0e2dbd740d6b160bc74007","observation_id":"52b9a243-1cf8-4085-b36a-135a9bc0147c","resolution":{"observed_at":"2026-08-09T19:42:54.222182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.13187","last_updated":"2026-05-16T13:21:33Z","snapshot_observed_at":"2026-08-02T22:48:02.475368Z","submitted_at":"2024-06-19T03:35:26Z","title":"Decouple then Converge: Handling Unknown Unlabeled Distributions in Long-Tailed Semi-Supervised Learning","version":2},"cited_work":{"arxiv_id":"2406.13187","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.13187","snapshot_observed_at":"2026-08-09T19:42:54.660223Z","title":"Decouple then Converge: Handling Unknown Unlabeled Distributions in Long-Tailed Semi-Supervised Learning","venue":"cs.LG","work_id":"8d91511d-8ac8-4405-99a9-e24156ecd5c7","year":2024},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.226941Z"},"links":{"cited_paper":"/paper/2406.13187","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:263e955b096c0c2a562fdb2e7ec912a9e96fa1d4684ad6e6e981522024c17f7d","observation_id":"f2810125-941b-4ebc-914b-90918bbec0ff","resolution":{"observed_at":"2026-08-09T19:42:54.666038Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.232360Z","title":"A unified view of label shift estimation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.232360Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:a9806ec0c142feddc1887c236a59ebc0e299e4fc8d52b1790a924f5047bf61ac","observation_id":"21001d9e-034d-4173-ad74-82d428b887f9","resolution":{"observed_at":"2026-08-09T19:42:54.232360Z","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":"2026-08-09T19:42:54.237968Z","title":"and Bengio, Y","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.237968Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:308bed16d785757fcc62ce3fbfaf9a673de524541e5499915559819cafc3b9b0","observation_id":"3e836971-aec7-4f61-b5ec-f4903fb38c1d","resolution":{"observed_at":"2026-08-09T19:42:54.237968Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.14923","last_updated":"2022-06-29T22:01:29Z","snapshot_observed_at":"2026-08-04T05:32:09.050958Z","submitted_at":"2022-06-29T22:01:29Z","title":"On Non-Random Missing Labels in Semi-Supervised Learning","version":1},"cited_work":{"arxiv_id":"2206.14923","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.14923","snapshot_observed_at":"2026-08-09T19:42:54.636213Z","title":"On Non-Random Missing Labels in Semi-Supervised Learning","venue":"cs.CV","work_id":"3eac19a8-fceb-41d4-932e-5b69b17fdb0d","year":2022},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.242971Z"},"links":{"cited_paper":"/paper/2206.14923","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:77d912288e49233b07b901726da4291d2d3d3fc43ceb76e8317f1c1febbad965","observation_id":"bc9ae131-7a9e-41d5-a92d-a28b5eb574d6","resolution":{"observed_at":"2026-08-09T19:42:54.641997Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.153319Z","title":null,"venue":null,"work_id":"5627e1e6-71b6-4edd-abf2-3b2b12936603","year":1996},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.248386Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:eec172229271addc77a0b6deb6a21ea66deeb24910ec8d1e6c7ef3b94984fc95","observation_id":"f53c712b-9669-4847-8abf-b22d9e3e5c89","resolution":{"observed_at":"2026-08-09T19:42:55.158333Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.136438Z","title":"Deepmatch: Balancing deep covariate representations for causal inference using adversarial training","venue":null,"work_id":"23b9acad-8d8a-450c-b7c9-cbe9f2628805","year":2020},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.253331Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:0558b240ae02a1d1f0debc16878e247e6751bd6bfa4cd22af37b7edc764f4803","observation_id":"3b9c65b6-440e-4029-9c11-ac524317da82","resolution":{"observed_at":"2026-08-09T19:42:55.142291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.258292Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.258292Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:e888a75c2141e1d4b9f22a7e6d4062664e92838989ff273dca8f387d827b9ea4","observation_id":"0bfd2d47-2ece-4259-b226-8247a24bd5ab","resolution":{"observed_at":"2026-08-09T19:42:54.258292Z","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":"2026-08-09T19:42:54.263198Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.263198Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:3396cf1c3851d30aa9f45e937f0f84435fd4ee7c45931e6e5ca008ea2d9ba4fe","observation_id":"fea6c41b-6f24-4b78-b1bf-37be73df8a04","resolution":{"observed_at":"2026-08-09T19:42:54.263198Z","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":"2026-08-09T19:42:55.099250Z","title":"J., and Shin, J","venue":null,"work_id":"583b9183-539b-4cd7-8426-8a2f9a2c0b02","year":2020},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.268017Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:46a58b28d2df0e3cfc2f563a952daf29dc8f4fc7854b59013ec14117f583df88","observation_id":"c14d6034-dd4b-4773-8d72-74d10a3b870c","resolution":{"observed_at":"2026-08-09T19:42:55.103821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.084477Z","title":"and Hinton, G","venue":null,"work_id":"0694fa86-2920-4e74-bbc1-c5b7e55bc35c","year":2009},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.273083Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:71cc00873fa22ad2425caab160d0b2fbfdf5e7967a3a9138737f1c1c4ecacb12","observation_id":"0381e75f-5b5e-46cb-8ecf-0a46becf20e1","resolution":{"observed_at":"2026-08-09T19:42:55.088923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02242","last_updated":"2017-03-15T14:22:41Z","snapshot_observed_at":"2026-08-01T18:35:12.430501Z","submitted_at":"2016-10-07T12:15:42Z","title":"Temporal Ensembling for Semi-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02242","snapshot_observed_at":"2026-08-09T19:42:54.288427Z","title":"and Aila, T","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.288427Z"},"links":{"cited_paper":"/paper/1610.02242","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:e4d0a4f20a930b6eef6e72c03ed2ea5b8a3a7a286ea43337f1dee88408d76235","observation_id":"06ede96c-ebb5-4775-80bc-a0fcae2e66fc","resolution":{"observed_at":"2026-08-09T19:42:54.288427Z","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":"2026-08-09T19:42:54.294516Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.294516Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:c6d49ae062f690961e955084d4923ca936f46947944352b851735d6bd8a5baa3","observation_id":"d5eb47a9-ed37-40c9-b4c6-c8ab48c586fa","resolution":{"observed_at":"2026-08-09T19:42:54.294516Z","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":"2026-08-09T19:42:55.058607Z","title":"Abc: Auxiliary balanced classifier for class-imbalanced semi-supervised learning","venue":null,"work_id":"cd36a7b1-ce8a-42d1-8896-9061076ef125","year":2021},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.300537Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:8821783ef092794fbbe48459a4c2706ab42a947f7b229d75ae6ae79fe97d8085","observation_id":"9b2cf12c-9124-4e72-9654-cd2c7e992a9b","resolution":{"observed_at":"2026-08-09T19:42:55.063401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.306331Z","title":"Detecting and correcting for label shift with black box predictors","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.306331Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:d8502a997d8b37dd3527e55166a12333a058df9d79ca4efce2cccb87bee500e5","observation_id":"9345e5bc-001a-44fc-8c63-4266d04bf88e","resolution":{"observed_at":"2026-08-09T19:42:54.306331Z","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":"2026-08-09T19:42:55.028658Z","title":"Three heads are better than one: Complementary experts for long-tailed semi-supervised learning","venue":null,"work_id":"fb04122f-df7a-4bcd-83d9-10c2d95492c3","year":2024},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.312003Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:83bb7cbeb40b5356e3817c7da29094eea6d94674c6008a57b9591f8dc1c4122c","observation_id":"0e328fd9-80fd-4859-8db3-749ff638f232","resolution":{"observed_at":"2026-08-09T19:42:55.034836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.07314","last_updated":"2021-07-09T21:23:25Z","snapshot_observed_at":"2026-08-08T11:03:52.719052Z","submitted_at":"2020-07-14T19:27:13Z","title":"Long-tail learning via logit adjustment","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.07314","snapshot_observed_at":"2026-08-09T19:42:54.317448Z","title":"K., Jayasumana, S., Rawat, A","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.317448Z"},"links":{"cited_paper":"/paper/2007.07314","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:4bd1d5a079e2c89584d4c18cc3cfe0e8040d2693b21b89e41fb008284be7e183","observation_id":"99139c97-745a-45f9-9d2d-8c93faaa5cea","resolution":{"observed_at":"2026-08-09T19:42:54.317448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18261","last_updated":"2023-10-27T16:50:13Z","snapshot_observed_at":"2026-07-06T16:39:36.790016Z","submitted_at":"2023-10-27T16:50:13Z","title":"Label Shift Estimators for Non-Ignorable Missing Data","version":1},"cited_work":{"arxiv_id":"2310.18261","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.18261","snapshot_observed_at":"2026-08-09T19:42:54.573342Z","title":"Label Shift Estimators for Non-Ignorable Missing Data","venue":"stat.ME","work_id":"412c6a27-25e2-44b3-a37c-62e7e246ed26","year":2023},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.323004Z"},"links":{"cited_paper":"/paper/2310.18261","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:1f5d3f406f3cf73f3543ad5dc7a9f61440e4a28cb610710b48591a1ba371b415","observation_id":"9ac82d5f-b108-45f7-b23a-5fcda73e5163","resolution":{"observed_at":"2026-08-09T19:42:54.579358Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:55.008727Z","title":null,"venue":null,"work_id":"6a8926fb-3fd9-4cbe-9cc8-cbd421997b42","year":1998},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.329167Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:e8fbb439eae6880b7f0eb24fd30363bfe3621c661600abb567b6220f0feb74d3","observation_id":"1f8e5c37-1b06-418c-afaa-719971b88808","resolution":{"observed_at":"2026-08-09T19:42:55.014756Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.05996","last_updated":"2024-09-09T18:45:43Z","snapshot_observed_at":"2026-07-06T19:12:49.406398Z","submitted_at":"2024-09-09T18:45:43Z","title":"Adapting to Shifting Correlations with Unlabeled Data Calibration","version":1},"cited_work":{"arxiv_id":"2409.05996","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.05996","snapshot_observed_at":"2026-08-09T19:42:54.549845Z","title":"Adapting to Shifting Correlations with Unlabeled Data Calibration","venue":"cs.LG","work_id":"aca9fbf4-4257-40fc-b44e-50a98f5aaa6a","year":2024},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.338760Z"},"links":{"cited_paper":"/paper/2409.05996","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:66dcb8aaa0f3df249732eaca0ae70f32054f612695d29d6cb5770a5a9e1e1462","observation_id":"d85523d7-5588-43ba-a1a3-cdc9d2e269ad","resolution":{"observed_at":"2026-08-09T19:42:54.555599Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.991099Z","title":null,"venue":null,"work_id":"ac3d2dc9-f819-4c84-8146-06178ee0daa2","year":2022},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.344676Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:fea923e1a310ae25e3dc144a97796bc3626cd8c7614ad21918f395d2963102c3","observation_id":"88710594-0827-4c7f-a2b5-a23ec27390df","resolution":{"observed_at":"2026-08-09T19:42:54.996349Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02278","last_updated":"2024-05-15T19:38:45Z","snapshot_observed_at":"2026-08-06T14:51:31.617873Z","submitted_at":"2023-10-01T00:04:29Z","title":"A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects","version":2},"cited_work":{"arxiv_id":"2310.02278","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.02278","snapshot_observed_at":"2026-08-09T19:42:54.526061Z","title":"A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects","venue":"stat.ME","work_id":"36c37b72-d47d-4e0b-a7b6-a51b737042a1","year":2023},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.350154Z"},"links":{"cited_paper":"/paper/2310.02278","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:70ace6b997ea18be9535c28e1a5f947af7e5839b3a0eace50101f7bdaa903b24","observation_id":"e4830ca8-bc26-48a0-80e0-3f5128530af5","resolution":{"observed_at":"2026-08-09T19:42:54.532113Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.973695Z","title":"Balanced meta-softmax for long-tailed visual recognition","venue":null,"work_id":"8444ca00-4836-488a-a335-5ff4caf252ca","year":2020},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.355943Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:fabf8b5157be84151f17ac6fb8e49f77038358287c148ab36ec5e94db6e396c8","observation_id":"bb7b143f-c995-4c00-b1d2-8bebeb77ed08","resolution":{"observed_at":"2026-08-09T19:42:54.979332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.956158Z","title":null,"venue":null,"work_id":"2463bc84-e6fa-4e4b-935c-8383d71a806c","year":1976},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.361170Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:6817f5f36a4aef1f4bd990490fd79c76bb91a0eaaa5efb48189abb9e96fec2af","observation_id":"a53a7e41-68a1-4d0e-a664-c7c20a0e892e","resolution":{"observed_at":"2026-08-09T19:42:54.961035Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.939051Z","title":"Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure","venue":null,"work_id":"043ba584-6b31-4784-ab14-dae4dd3787d3","year":2002},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.366460Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:31d9e350a2206d06f0cc90e44097b90e452092fddd704480fcf6010d84ce3268","observation_id":"9ad0db48-e83c-47d1-9cc0-c7b6c09b5303","resolution":{"observed_at":"2026-08-09T19:42:54.945067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.07512","last_updated":"2023-03-03T17:30:31Z","snapshot_observed_at":"2026-07-06T12:47:52.082416Z","submitted_at":"2022-03-14T21:42:21Z","title":"Don't fear the unlabelled: safe semi-supervised learning via simple debiasing","version":3},"cited_work":{"arxiv_id":"2203.07512","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.07512","snapshot_observed_at":"2026-08-09T19:42:54.498226Z","title":"Don't fear the unlabelled: safe semi-supervised learning via simple debiasing","venue":"stat.ML","work_id":"1ae14ee9-96ea-4d01-ba2e-a4c17f76229a","year":2022},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.371577Z"},"links":{"cited_paper":"/paper/2203.07512","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:c4198f0e2db242493124f9491d85f448a2053acf993678a886113dfba2a69257","observation_id":"35c796dc-610f-417c-a8c8-8fa9125d6391","resolution":{"observed_at":"2026-08-09T19:42:54.506305Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.376348Z","title":"Adapting neural networks for the estimation of treatment effects","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.376348Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:27be082eba7bde37a44e4709cf7476b0d16db66989c54c158ed3253fdff72b6c","observation_id":"5311894b-8aa3-4122-900d-998e48e9f190","resolution":{"observed_at":"2026-08-09T19:42:54.376348Z","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":"2026-08-09T19:42:54.380848Z","title":"A., Cubuk, E","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.380848Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:a601ba4ad92ee000a00e5c1a7f63ed418b3a916fff9fce4d6691c29c4bbc893d","observation_id":"5e8f1ad8-1cc9-462c-83db-28b1aed00bf6","resolution":{"observed_at":"2026-08-09T19:42:54.380848Z","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":"2026-08-09T19:42:54.899504Z","title":"Are labels informative in semi-supervised learning? estimating and leveraging the missing-data mechanism","venue":null,"work_id":"75d3bf2a-0600-4e85-ad7a-e3de20a1dc46","year":2023},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.385281Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:bd5f85975111cd5cb7a1808d4edb31f3fd638537aadeb3dbc00b7059af6bc36e","observation_id":"1649f588-439d-4cff-a376-3cfc78acfe4f","resolution":{"observed_at":"2026-08-09T19:42:54.905295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.882838Z","title":"P., Ebrahimi, S., and D'Amour, A","venue":null,"work_id":"c56256c9-e544-4038-af5f-0de6d8025193","year":2023},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.389689Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:56bb4eee0427089d500f8ac1a659f126e9e22b34094c0f7f2e8b58d37ff377d7","observation_id":"20c10bda-5d9f-4c1e-952e-19477d6ea1e9","resolution":{"observed_at":"2026-08-09T19:42:54.888036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.865391Z","title":null,"venue":null,"work_id":"c6660534-b970-4869-a628-36a7c3ae2c8f","year":2006},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.393904Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:09a1b88dbf03df1fa32a19c87406bad95739598f70e828fddc2c44f6fbddab27","observation_id":"a7041392-8a8a-47ce-bcd3-c62e8bcd63d2","resolution":{"observed_at":"2026-08-09T19:42:54.870422Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.848662Z","title":null,"venue":null,"work_id":"b40f1256-0869-415d-a24d-13edeca6d51d","year":2000},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.397969Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:fbb7d1199149711bc62a2ad6112dd22b4d028dd1e987b5e2883697bfe0668f69","observation_id":"5fd279f5-87c1-4d82-931c-e6a0996e6f96","resolution":{"observed_at":"2026-08-09T19:42:54.853788Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.402083Z","title":"and Athey, S","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.402083Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:2ab142e88532a23486d4ab866553def7267c135809fd42809b7761e91d40f350","observation_id":"18c07d6f-6fa0-4a2b-8d39-d7e923d9974d","resolution":{"observed_at":"2026-08-09T19:42:54.402083Z","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":"2026-08-09T19:42:54.817516Z","title":"Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning","venue":null,"work_id":"e92f195f-7db6-4c2e-b077-d882073538fc","year":2021},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.406465Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:f5d6cda57e40b737215094081185557f4bb63a5429bd262d6f22b88bad290737","observation_id":"6fb03c13-a5c1-4f44-ba05-6049aeb088f4","resolution":{"observed_at":"2026-08-09T19:42:54.823929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.799876Z","title":"and Gan, K","venue":null,"work_id":"76a7e94d-c17d-45c4-ba50-02936ea3f69c","year":2023},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.410555Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:7b8cdb6ee76b6af9796d98a32b4d85c8722886ded91c1f2101070398e722ee05","observation_id":"df923fd2-71e0-4994-abef-8f82ec1664e6","resolution":{"observed_at":"2026-08-09T19:42:54.805150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.781816Z","title":"Learning label shift correction for test-agnostic long-tailed recognition","venue":null,"work_id":"56583224-0ba1-44c7-8d90-61ef39efa5af","year":2024},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.417542Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:8b7742d3b0627d4c871778c1ca49821b75bac146d4c5671d08f48b859234400b","observation_id":"74817487-c1ca-4b59-b2a9-9525ebe3167d","resolution":{"observed_at":"2026-08-09T19:42:54.787440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.423011Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.423011Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:f0d5026ac3eaad40d7888f6010fac105a4f69559485ad1740943254198a06380","observation_id":"536ed74d-4a7a-432a-bf56-b8781b875bea","resolution":{"observed_at":"2026-08-09T19:42:54.423011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00809","last_updated":"2024-06-03T22:32:38Z","snapshot_observed_at":"2026-08-10T06:22:44.197067Z","submitted_at":"2023-10-01T22:28:34Z","title":"Towards Causal Foundation Model: on Duality between Causal Inference and Attention","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00809","snapshot_observed_at":"2026-08-09T19:42:54.428521Z","title":"Towards causal foundation model: on duality between causal inference and attention","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.428521Z"},"links":{"cited_paper":"/paper/2310.00809","citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:97557499eb28023d698284e3b15928de21ec5edb88a1d430c21ac4a66e6833e9","observation_id":"5e97ec8d-0aba-4751-a7ec-51bae2496a4a","resolution":{"observed_at":"2026-08-09T19:42:54.428521Z","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":"2026-08-09T19:42:54.748741Z","title":"Dc-ssl: Addressing mismatched class distribution in semi-supervised learning","venue":null,"work_id":"1e40abeb-28f6-487d-bbee-3a415930122d","year":2022},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.434058Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:d05c1ae23eb366c4889b6c09a0a714ae8e47a6ab6c5874cb2a37670dab161128","observation_id":"6dfff1e6-2c79-4032-920c-bb1562e51e0c","resolution":{"observed_at":"2026-08-09T19:42:54.759527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T19:42:54.733272Z","title":"Doubly-robust self-training","venue":null,"work_id":"e5592297-879a-45e6-968c-0d7cbd429787","year":2024},"citing_paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-09T19:42:54.438936Z"},"links":{"citing_paper":"/paper/2502.00279"},"observation_digest":"sha256:4712d722b869cd8494ea3bf89bd1393149faed08885f97686d4fc2ff4bd26814","observation_id":"864e832c-308f-4e07-a4f9-dc04473b58d8","resolution":{"observed_at":"2026-08-09T19:42:54.737944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.00279","last_updated":"2025-02-01T02:34:12Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T23:11:43.874800Z","submitted_at":"2025-02-01T02:34:12Z","title":"Improving realistic semi-supervised learning with doubly robust estimation"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":28,"verified_exact":4,"verified_fuzzy":22},"total_outbound_references":56},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2502.00279."}