{"as_of":"2026-08-09T23:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7422f4c4292575bef08f0190a6f2035ca300c12cc02e5165b0e2612890a17da2","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T19:42:54.066532Z","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":56,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"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":"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":"2026-08-06T22:42:23.415733Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.21190","last_updated":"2025-06-26T12:49:02Z","snapshot_observed_at":"2026-08-09T16:18:23.363480Z","submitted_at":"2025-06-26T12:49:02Z","title":"Survival analysis under label shift","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:42:23.415733Z"},"links":{"cited_paper":"/paper/1903.09734","citing_paper":"/paper/2506.21190"},"observation_digest":"sha256:58878a9239475327df2cb7d8bbc9b82732cc2deb136049533bd6c6b05b0d67c3","observation_id":"292bb9aa-9aaf-4b21-b3ff-f103cb3de34d","resolution":{"observed_at":"2026-08-06T22:42:23.415733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T18:11:39.699786Z","title":"arXiv preprint arXiv:1903.09734","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2507.09222","last_updated":"2025-07-20T07:22:45Z","snapshot_observed_at":"2026-08-09T19:01:30.456711Z","submitted_at":"2025-07-12T09:39:07Z","title":"Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T18:11:39.699786Z"},"links":{"cited_paper":"/paper/1903.09734","citing_paper":"/paper/2507.09222"},"observation_digest":"sha256:0405904066af8eb0b4de953dfa675d50463b4bdd2fa0aba5e3e7800599360ab8","observation_id":"bd872ced-79c9-4455-9856-9a8933ac27c4","resolution":{"observed_at":"2026-08-06T18:11:39.699786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"1903.09734","doi":"10.48550/arxiv.1903.09734","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.09734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Regularized","venue":"arXiv (Cornell University)","work_id":"232b5583-0253-4b1b-9923-c2f336610430","year":1903},"citing_paper":{"arxiv_id":"2605.04363","last_updated":"2026-05-24T03:30:57Z","snapshot_observed_at":"2026-07-06T23:17:09.246351Z","submitted_at":"2026-05-06T00:01:47Z","title":"Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-08T18:28:39.171830Z"},"links":{"cited_paper":"/paper/1903.09734","citing_paper":"/paper/2605.04363"},"observation_digest":"sha256:114242de74439ae9791a6aa13aaa9f33ed3b32c84e916c8f5d6094e1cad4d242","observation_id":"7a08f7eb-6f21-44ca-96cc-ab67e44f6a48","resolution":{"observed_at":"2026-05-09T06:25:44.726482Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"1903.09734","doi":"10.48550/arxiv.1903.09734","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.09734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Regularized","venue":"arXiv (Cornell University)","work_id":"232b5583-0253-4b1b-9923-c2f336610430","year":1903},"citing_paper":{"arxiv_id":"2605.04363","last_updated":"2026-05-24T03:30:57Z","snapshot_observed_at":"2026-07-06T23:17:09.246351Z","submitted_at":"2026-05-06T00:01:47Z","title":"Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustment","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-01T00:30:39.185222Z"},"links":{"cited_paper":"/paper/1903.09734","citing_paper":"/paper/2605.04363"},"observation_digest":"sha256:3794c4c50fdcbb43db0c06ec6587149b11869c2f1b3419fb8d33ad412a83892c","observation_id":"a249ba17-3408-474f-bb02-78f1deb3e38b","resolution":{"observed_at":"2026-07-01T00:35:10.309950Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"1903.09734","doi":"10.48550/arxiv.1903.09734","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.09734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Regularized","venue":"arXiv (Cornell University)","work_id":"232b5583-0253-4b1b-9923-c2f336610430","year":1903},"citing_paper":{"arxiv_id":"2605.05591","last_updated":"2026-05-07T02:17:53Z","snapshot_observed_at":"2026-07-06T23:18:12.631820Z","submitted_at":"2026-05-07T02:17:53Z","title":"In-Context Positive-Unlabeled Learning","version":1},"reference_index":109,"source":"arxiv_source","source_observed_at":"2026-05-08T05:55:51.227763Z"},"links":{"cited_paper":"/paper/1903.09734","citing_paper":"/paper/2605.05591"},"observation_digest":"sha256:53646545ad47bf8c69363d61bab1c47ef50d0be76101c8eb9ef96a6b7f4c319c","observation_id":"5c099094-b3cd-4898-b882-ea4fd834dfd9","resolution":{"observed_at":"2026-05-08T23:29:25.941639Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"1903.09734","doi":"10.48550/arxiv.1903.09734","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.09734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Regularized","venue":"arXiv (Cornell University)","work_id":"232b5583-0253-4b1b-9923-c2f336610430","year":1903},"citing_paper":{"arxiv_id":"2605.09471","last_updated":"2026-05-10T10:56:59Z","snapshot_observed_at":"2026-08-05T20:10:28.703944Z","submitted_at":"2026-05-10T10:56:59Z","title":"The Statistical Cost of Adaptation in Multi-Source Transfer Learning","version":1},"reference_index":242,"source":"arxiv_source","source_observed_at":"2026-05-12T04:19:05.837824Z"},"links":{"cited_paper":"/paper/1903.09734","citing_paper":"/paper/2605.09471"},"observation_digest":"sha256:51d0cf09211109d0d4d94a488bd658c9a6f58a032a6f2ccb244e36aa3db15e82","observation_id":"83411548-201a-409e-b26a-b219c5394a1b","resolution":{"observed_at":"2026-05-12T06:21:28.835281Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"1903.09734","doi":"10.48550/arxiv.1903.09734","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.09734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Regularized","venue":"arXiv (Cornell University)","work_id":"232b5583-0253-4b1b-9923-c2f336610430","year":1903},"citing_paper":{"arxiv_id":"2606.00797","last_updated":"2026-05-30T16:35:45Z","snapshot_observed_at":"2026-07-06T23:41:30.158879Z","submitted_at":"2026-05-30T16:35:45Z","title":"Robust inference for risk heterogeneity under group imbalance","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-06-28T18:18:35.249341Z"},"links":{"cited_paper":"/paper/1903.09734","citing_paper":"/paper/2606.00797"},"observation_digest":"sha256:9d752742333eb0ac223333f1febf52aa1cacf4340fe33d00483f33605bb12494","observation_id":"9ba6b020-e7e2-46c9-9fc0-9d866fbbf753","resolution":{"observed_at":"2026-06-28T20:52:38.071288Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"1903.09734","doi":"10.48550/arxiv.1903.09734","metadata_source":"arxiv_reference","pith_arxiv_id":"1903.09734","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Regularized","venue":"arXiv (Cornell University)","work_id":"232b5583-0253-4b1b-9923-c2f336610430","year":1903},"citing_paper":{"arxiv_id":"2606.22917","last_updated":"2026-06-22T06:57:30Z","snapshot_observed_at":"2026-08-08T12:37:34.262584Z","submitted_at":"2026-06-22T06:57:30Z","title":"GRAIN: Group Aggregation via Min-Norm Objective","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-06-26T09:18:55.049767Z"},"links":{"cited_paper":"/paper/1903.09734","citing_paper":"/paper/2606.22917"},"observation_digest":"sha256:d2f69fb71da1c649c2229e52932ed0fefbb2422df8afbe9c2836f1207c88f9ac","observation_id":"03bb8f50-8497-49b2-97f6-aa1d7c3e2554","resolution":{"observed_at":"2026-07-04T09:59:45.275609Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1903.09734/citation-record","integrity":"/paper/1903.09734/integrity","json":"/paper/1903.09734/citation-record.json","paper":"/paper/1903.09734"},"outbound":[],"paper":{"arxiv_id":"1903.09734","last_updated":"2019-03-22T23:46:24Z","latest_version":1,"primary_category":"cs.LG","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"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1903.09734."}