{"as_of":"2026-08-16T11:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:916cca22eb7416794d19a77c237e23c688471bb7f7d614506aa54a3fbda56a76","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:51:59.570488Z","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":23,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-14T10:42:14.488594Z","title":"Duchi and H","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.10763","last_updated":"2019-11-25T04:22:40Z","snapshot_observed_at":"2026-08-15T08:25:24.280589Z","submitted_at":"2019-08-28T15:02:45Z","title":"Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-14T10:42:14.488594Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/1908.10763"},"observation_digest":"sha256:0076d88742451a241b47f282815e35fee897bc33adec000778334a9ac207baa4","observation_id":"48800741-b88a-4e9e-8be2-97d02dbd4999","resolution":{"observed_at":"2026-08-14T10:42:14.488594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-14T05:05:53.230076Z","title":"Duchi and H","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1909.02060","last_updated":"2019-09-04T19:07:33Z","snapshot_observed_at":"2026-08-14T04:58:55.958441Z","submitted_at":"2019-09-04T19:07:33Z","title":"Distributionally Robust Language Modeling","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-14T05:05:53.230076Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/1909.02060"},"observation_digest":"sha256:a7ef98f483d44a2e2a4671bfe538aea4f69fe096ad25f441c495192d9d052c6a","observation_id":"4f756f46-1d71-4597-800d-234ea54853f5","resolution":{"observed_at":"2026-08-14T05:05:53.230076Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":"1810.08750","doi":"10.48550/arxiv.1810.08750","metadata_source":"arxiv_reference","pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Duchi and H","venue":"arXiv (Cornell University)","work_id":"783ce78b-8954-44f5-9071-8c8043475494","year":2018},"citing_paper":{"arxiv_id":"1911.08731","last_updated":"2020-04-02T05:40:29Z","snapshot_observed_at":"2026-08-14T08:06:00.989531Z","submitted_at":"2019-11-20T06:43:41Z","title":"Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-13T09:18:32.912629Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/1911.08731"},"observation_digest":"sha256:b27c957564bc76ee6fb98677b82937d6dc4dbfec442420b05f240ee366d60723","observation_id":"60121da8-7218-4374-82b5-f9663a312040","resolution":{"observed_at":"2026-05-13T09:18:33.081985Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":"1810.08750","doi":"10.48550/arxiv.1810.08750","metadata_source":"arxiv_reference","pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Duchi and H","venue":"arXiv (Cornell University)","work_id":"783ce78b-8954-44f5-9071-8c8043475494","year":2018},"citing_paper":{"arxiv_id":"2503.15105","last_updated":"2026-05-19T19:16:21Z","snapshot_observed_at":"2026-08-13T18:28:53.474750Z","submitted_at":"2025-03-19T11:04:36Z","title":"Control, Optimal Transport and Neural Differential Equations in Supervised Learning","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-22T23:50:21.087380Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/2503.15105"},"observation_digest":"sha256:f6c0ce2b43466bb2986a35007ec3c388203a830018979e465bfec5d34da94688","observation_id":"fe710e65-1c93-4e76-a460-a72f96f00090","resolution":{"observed_at":"2026-05-22T23:52:16.949650Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-16T05:51:59.570488Z","title":"Learning models with uniform perfor- mance via distributionally robust optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.19792","last_updated":"2025-04-28T13:36:28Z","snapshot_observed_at":"2026-08-16T05:41:14.814181Z","submitted_at":"2025-04-28T13:36:28Z","title":"Contextures: The Mechanism of Representation Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T05:51:59.570488Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/2504.19792"},"observation_digest":"sha256:d477a68125b90c3b909a1a268e97cbf2feb240ab77788c92344500bc9a64a7d2","observation_id":"8e8a93b5-2f38-48b0-b4fa-e96813b552af","resolution":{"observed_at":"2026-08-16T05:51:59.570488Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-15T20:33:16.088211Z","title":"Learning models with uniform performance via distributionally robust optimization","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.12583","last_updated":"2025-05-30T07:28:49Z","snapshot_observed_at":"2026-08-15T20:28:36.825216Z","submitted_at":"2025-05-19T00:11:42Z","title":"A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T20:33:16.088211Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/2505.12583"},"observation_digest":"sha256:712d930293384879bb9e295d20e6ccaa76a3beddeec5054bbf3cea326b2a791d","observation_id":"a06ebcf7-86d5-44db-b455-343d1ee3aa06","resolution":{"observed_at":"2026-08-15T20:33:16.088211Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-07T14:04:23.320223Z","title":"Learning models with uniform performance via distributionally robust optimization, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.20380","last_updated":"2025-05-26T17:32:14Z","snapshot_observed_at":"2026-08-16T06:54:33.736857Z","submitted_at":"2025-05-26T17:32:14Z","title":"GRAPE: Optimize Data Mixture for Group Robust Multi-target Adaptive Pretraining","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T14:04:23.320223Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/2505.20380"},"observation_digest":"sha256:1902022b065278f8f8226c6643f821c9c378062be189cf7ae102e4f6136721a4","observation_id":"890473d1-52e4-421e-9104-c33da6a6e0ee","resolution":{"observed_at":"2026-08-07T14:04:23.320223Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":"1810.08750","doi":"10.48550/arxiv.1810.08750","metadata_source":"arxiv_reference","pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Duchi and H","venue":"arXiv (Cornell University)","work_id":"783ce78b-8954-44f5-9071-8c8043475494","year":2018},"citing_paper":{"arxiv_id":"2605.03125","last_updated":"2026-05-06T22:03:28Z","snapshot_observed_at":"2026-08-15T17:50:58.873332Z","submitted_at":"2026-05-04T20:04:52Z","title":"Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-08T19:13:15.234351Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/2605.03125"},"observation_digest":"sha256:d1e01f2cb9fdf2c7472808ea52952edda8a8abf04fd1314920f081444a80925a","observation_id":"e8a8b78a-c191-48f3-bf02-234e66e4077e","resolution":{"observed_at":"2026-05-09T06:00:35.940148Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":"1810.08750","doi":"10.48550/arxiv.1810.08750","metadata_source":"arxiv_reference","pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Duchi and H","venue":"arXiv (Cornell University)","work_id":"783ce78b-8954-44f5-9071-8c8043475494","year":2018},"citing_paper":{"arxiv_id":"2606.09881","last_updated":"2026-06-03T05:44:29Z","snapshot_observed_at":"2026-08-12T12:37:31.669797Z","submitted_at":"2026-06-03T05:44:29Z","title":"Toward Calibrated, Fair, and accurate Deepfake Detection","version":1},"reference_index":160,"source":"arxiv_source","source_observed_at":"2026-06-28T07:05:18.026601Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/2606.09881"},"observation_digest":"sha256:5252078ba5809c1cc580e511df14f18604ec46d2781ba687608acec810bfc955","observation_id":"ffbf9b3b-e3a8-4419-92a0-c754d76d8f03","resolution":{"observed_at":"2026-06-28T07:11:45.127770Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-07-14T15:21:29.503625Z","title":"The Annals of Statistics , volume =","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09816","last_updated":"2026-07-21T10:33:53Z","snapshot_observed_at":"2026-08-15T01:15:26.511515Z","submitted_at":"2026-07-10T06:37:12Z","title":"RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-07-14T15:21:29.503625Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/2607.09816"},"observation_digest":"sha256:8a8c4dcefdc77b9ae8e3a6e24007caa4d742c4518288b4fbf4b5eb96652fc5f6","observation_id":"0dc87e4e-7fbd-4999-8c26-3d9634041b2f","resolution":{"observed_at":"2026-07-14T15:21:29.503625Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.08750","snapshot_observed_at":"2026-08-02T07:46:24.648620Z","title":"The Annals of Statistics , volume =","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09816","last_updated":"2026-07-21T10:33:53Z","snapshot_observed_at":"2026-08-15T01:15:26.511515Z","submitted_at":"2026-07-10T06:37:12Z","title":"RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-02T07:46:24.648620Z"},"links":{"cited_paper":"/paper/1810.08750","citing_paper":"/paper/2607.09816"},"observation_digest":"sha256:d73e7d877cd92d9f27bf75f932e7885e49ef5463aab9304b858b92d30108fed5","observation_id":"cfea67a3-6188-4999-b02f-b8e268abf941","resolution":{"observed_at":"2026-08-02T07:46:24.648620Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1810.08750/citation-record","integrity":"/paper/1810.08750/integrity","json":"/paper/1810.08750/citation-record.json","paper":"/paper/1810.08750"},"outbound":[],"paper":{"arxiv_id":"1810.08750","last_updated":"2020-07-18T01:20:20Z","latest_version":6,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-14T18:11:52.942637Z","submitted_at":"2018-10-20T03:50:29Z","title":"Learning Models with Uniform Performance via Distributionally Robust Optimization"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1810.08750."}