{"as_of":"2026-08-09T22:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:063daca9933f15e6fffff978627f00db179300c63354cad69fec2809c3b70030","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:31:53.619123Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-11T02:05:53.638212Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T20:11:10.193830Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"cited_work":{"arxiv_id":"2507.08280","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.08280","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Arthur Dantas Mangussi, Ricardo Cardoso Pereira, Ana Carolina Lorena, and Pedro Henriques Abreu","venue":null,"work_id":"e9b4390c-69c6-475f-9dac-d498c50437e8","year":null},"citing_paper":{"arxiv_id":"2605.04323","last_updated":"2026-05-08T14:33:51Z","snapshot_observed_at":"2026-08-09T08:38:54.532386Z","submitted_at":"2026-05-05T21:57:44Z","title":"LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-08T17:40:50.204175Z"},"links":{"cited_paper":"/paper/2507.08280","citing_paper":"/paper/2605.04323"},"observation_digest":"sha256:801db6a9521ee97410dd33375280dcccda939d7da808a42f3d8a6288fcbd935d","observation_id":"d437d91b-cb17-4636-945f-9f9f91451eab","resolution":{"observed_at":"2026-05-11T17:21:08.700343Z","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":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"cited_work":{"arxiv_id":"2507.08280","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.08280","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Arthur Dantas Mangussi, Ricardo Cardoso Pereira, Ana Carolina Lorena, and Pedro Henriques Abreu","venue":null,"work_id":"e9b4390c-69c6-475f-9dac-d498c50437e8","year":null},"citing_paper":{"arxiv_id":"2605.04323","last_updated":"2026-05-08T14:33:51Z","snapshot_observed_at":"2026-08-09T08:38:54.532386Z","submitted_at":"2026-05-05T21:57:44Z","title":"LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-11T02:05:53.638212Z"},"links":{"cited_paper":"/paper/2507.08280","citing_paper":"/paper/2605.04323"},"observation_digest":"sha256:52e6fb0ca6c88c9f122d010f47db9684064040b4c81ab40139e0c3055088e1e4","observation_id":"cdc04833-b95c-413d-93a9-f5e952461b08","resolution":{"observed_at":"2026-05-11T03:55:57.463525Z","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":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"cited_work":{"arxiv_id":"2507.08280","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.08280","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Arthur Dantas Mangussi, Ricardo Cardoso Pereira, Ana Carolina Lorena, and Pedro Henriques Abreu","venue":null,"work_id":"e9b4390c-69c6-475f-9dac-d498c50437e8","year":null},"citing_paper":{"arxiv_id":"2605.06290","last_updated":"2026-05-07T13:56:49Z","snapshot_observed_at":"2026-07-06T23:18:46.145104Z","submitted_at":"2026-05-07T13:56:49Z","title":"Data Language Models: A New Foundation Model Class for Tabular Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-08T10:06:34.627253Z"},"links":{"cited_paper":"/paper/2507.08280","citing_paper":"/paper/2605.06290"},"observation_digest":"sha256:8bbdf0e889d27b4e5431c20ed8b00a65c3c0d55bf1a16c5ce7e084da46e00a15","observation_id":"a17c51e3-b8b6-4796-9011-fd1fcb7f6a7a","resolution":{"observed_at":"2026-05-11T20:11:10.200415Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2507.08280/citation-record","integrity":"/paper/2507.08280/integrity","json":"/paper/2507.08280/citation-record.json","paper":"/paper/2507.08280"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.239539Z","title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","venue":null,"work_id":"f01c229d-2d2a-4529-bc26-95589971fab6","year":2020},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.387537Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:9ba164d81c25dd1e27442ce59c490d7181d2f6fef30f1ea35145b38456a37886","observation_id":"4291577e-fb09-480a-a361-6796df3a89c2","resolution":{"observed_at":"2026-08-06T18:31:54.244029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.224103Z","title":"Ehrtemporalvariability: delineating temporal data-set shifts in electronic health records","venue":null,"work_id":"8f8bbafe-3d72-47ef-a0b4-3d3d7e7ae9f6","year":2020},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.426337Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:586ed6540607bb5707f9470dd32f657bb75151f069e0f6067d7e4f6705399810","observation_id":"43ab5e24-7cb1-4e57-9391-0c4525203f1a","resolution":{"observed_at":"2026-08-06T18:31:54.228542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.209566Z","title":"Domain adaptation under missingness shift","venue":null,"work_id":"d975cc64-05c9-4cba-b2c3-e428a765e039","year":2023},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.498217Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:37a711efaf944cbee7b2e982d413478fbf0c21e4b9d21409b816db71794e700f","observation_id":"67df0f20-cca0-4a22-ab6a-0f3e8ceeb5e4","resolution":{"observed_at":"2026-08-06T18:31:54.213914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2504.00322","last_updated":"2025-04-01T01:03:56Z","snapshot_observed_at":"2026-08-09T09:03:39.659517Z","submitted_at":"2025-04-01T01:03:56Z","title":"Domain Adaptation Under MNAR Missingness","version":1},"cited_work":{"arxiv_id":"2504.00322","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.00322","snapshot_observed_at":"2026-08-06T18:31:53.859433Z","title":"Domain Adaptation Under MNAR Missingness","venue":"stat.ME","work_id":"88c3bb15-2f58-4766-b6e6-eec4f700b680","year":2025},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.555923Z"},"links":{"cited_paper":"/paper/2504.00322","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:bc1250ea993e7d605e970d2c79a29632d663fd1b7d798c84c39ced2daa1696fd","observation_id":"921b37a3-563b-4d33-bab6-21a156071711","resolution":{"observed_at":"2026-08-06T18:31:53.864648Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.194954Z","title":"Managing dataset shift by adversarial validation for credit scoring","venue":null,"work_id":"3b5436a9-c669-4ca2-a932-3382a4ef7948","year":2022},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.604162Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:00fa2528a785a07adcf49b48c3eba3787987da595c614d35a0adde3fdc345653","observation_id":"f251e708-55dc-4672-b17c-a2224c0af98a","resolution":{"observed_at":"2026-08-06T18:31:54.199715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.181654Z","title":"Response rates, nonresponse bias, and data quality: results from a national survey of senior healthcare leaders","venue":null,"work_id":"a85f0cbc-3359-4a88-9514-da8377fd0ebe","year":2015},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.662778Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:041d6edad8a17a502c21356c5c601c3c8b2d4a1d19697ce6d584dffc2308c9d7","observation_id":"471cb338-f014-41bf-b308-149b327061d9","resolution":{"observed_at":"2026-08-06T18:31:54.185855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.166646Z","title":"Benchmarking distribution shift in tabular data with tableshift","venue":null,"work_id":"95f48e1e-4859-4d55-b894-fb048cda3d0d","year":2023},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.716601Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:0ddc9d6438884f2ae87d2192b900947374aaa5a7a89da431844ed853f9fddc10","observation_id":"107a9219-654f-4741-83cd-fbca4c0afff1","resolution":{"observed_at":"2026-08-06T18:31:54.171132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.150655Z","title":"A review and suggestions for synthetic data generation strategies using deep generative models","venue":null,"work_id":"6eee22e0-76a5-4f1c-a95b-aab9fb72dc45","year":2023},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.830580Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:ba2c4ec82a3b1c14d57f199c4881a3609e69b16b7baab0bddb4ceeb1ad7264dc","observation_id":"09cc57cb-1463-4117-91c5-6633ae23d2bc","resolution":{"observed_at":"2026-08-06T18:31:54.155789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2302.11777","last_updated":"2023-02-23T04:37:49Z","snapshot_observed_at":"2026-08-07T10:24:28.959505Z","submitted_at":"2023-02-23T04:37:49Z","title":"Embeddings for Tabular Data: A Survey","version":1},"cited_work":{"arxiv_id":"2302.11777","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.11777","snapshot_observed_at":"2026-08-06T18:31:53.838612Z","title":"Embeddings for Tabular Data: A Survey","venue":"cs.LG","work_id":"9cd96686-c367-4913-bd8f-013fb1e6c80a","year":2023},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.924558Z"},"links":{"cited_paper":"/paper/2302.11777","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:719df069c167f275f633df0095a0428411f5f7333b375020d5e0183c3a132493","observation_id":"96c7623d-1ab5-4690-8d00-77f90ca9a2ea","resolution":{"observed_at":"2026-08-06T18:31:53.843334Z","resolver_source":"local_arxiv","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":"2106.15147","last_updated":"2022-03-15T22:16:20Z","snapshot_observed_at":"2026-08-08T01:49:28.951153Z","submitted_at":"2021-06-29T08:08:33Z","title":"SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.15147","snapshot_observed_at":"2026-08-06T18:31:51.977787Z","title":"Scarf: Self-supervised contrastive learning using random feature corruption","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:51.977787Z"},"links":{"cited_paper":"/paper/2106.15147","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:bf383d8cbd6ceec923da800abc6b44348f1c391c610c7e4cb468e28341643762","observation_id":"e65ec71e-15c9-4f36-a766-efed9465cc64","resolution":{"observed_at":"2026-08-06T18:31:51.977787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18541","last_updated":"2023-12-18T15:41:50Z","snapshot_observed_at":"2026-08-08T12:04:15.177125Z","submitted_at":"2023-10-28T00:05:28Z","title":"ReConTab: Regularized Contrastive Representation Learning for Tabular Data","version":2},"cited_work":{"arxiv_id":"2310.18541","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.18541","snapshot_observed_at":"2026-08-06T18:31:53.799164Z","title":"ReConTab: Regularized Contrastive Representation Learning for Tabular Data","venue":"cs.LG","work_id":"1c66d54e-f1bb-4718-ae08-1dbda29b3173","year":2023},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.063698Z"},"links":{"cited_paper":"/paper/2310.18541","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:0e7caf95ed97206a61f20cc25af6ab05a4e42bcca680280cb8ac7964c3c0e92d","observation_id":"d283152a-73c9-4216-948f-a4cb855b744f","resolution":{"observed_at":"2026-08-06T18:31:53.805694Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.135970Z","title":"Switchtab: Switched autoencoders are effective tabular learners","venue":null,"work_id":"b57622a0-a80a-48e5-9fcb-fffc965eb280","year":2024},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.119555Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:8b0dd039d14350d1b9f3b5f7af484611f2643225572942d7c447e5f2ee43b69c","observation_id":"60fe556c-0913-4a1d-8dd7-6107f54808f8","resolution":{"observed_at":"2026-08-06T18:31:54.140455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.121075Z","title":"Revisiting deep learning models for tabular data","venue":null,"work_id":"254fac56-7d79-4d0f-a2c5-2c34e7299005","year":2021},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.197030Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:585fcecebf0d9cde6e9a31e1e844ab4f86bd9587f3271e705495827cbd384773","observation_id":"ecad1a18-bdde-42a6-b76f-6deebc26d435","resolution":{"observed_at":"2026-08-06T18:31:54.125720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2305.06090","last_updated":"2023-05-10T12:17:52Z","snapshot_observed_at":"2026-07-06T15:25:32.095335Z","submitted_at":"2023-05-10T12:17:52Z","title":"XTab: Cross-table Pretraining for Tabular Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06090","snapshot_observed_at":"2026-08-06T18:31:52.302791Z","title":"Xtab: Cross-table pretraining for tabular transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.302791Z"},"links":{"cited_paper":"/paper/2305.06090","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:694c8334b9a2d9355667efd508886a08666eeeb2af89f7386014ab1ec09f677a","observation_id":"3d9de762-0ded-4943-9372-cb2a49e6454f","resolution":{"observed_at":"2026-08-06T18:31:52.302791Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.01848","last_updated":"2023-09-16T09:33:32Z","snapshot_observed_at":"2026-07-06T13:27:49.894090Z","submitted_at":"2022-07-05T07:17:43Z","title":"TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.01848","snapshot_observed_at":"2026-08-06T18:31:52.459443Z","title":"Tabpfn: A transformer that solves small tabular classification problems in a second","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.459443Z"},"links":{"cited_paper":"/paper/2207.01848","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:01348407c1205927cccc5667feea5ef10525a35dd9788917e16427a75efa245f","observation_id":"992be7d8-cc50-49c4-80e8-037e7cf04995","resolution":{"observed_at":"2026-08-06T18:31:52.459443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.06678","last_updated":"2020-12-11T23:31:23Z","snapshot_observed_at":"2026-07-06T10:23:43.246202Z","submitted_at":"2020-12-11T23:31:23Z","title":"TabTransformer: Tabular Data Modeling Using Contextual Embeddings","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.06678","snapshot_observed_at":"2026-08-06T18:31:52.523321Z","title":"Tabtransformer: Tabular data modeling using contextual embeddings","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.523321Z"},"links":{"cited_paper":"/paper/2012.06678","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:b6e345475322d196ba91b4312c52421b207e1f75605e8079b784034a55752b0c","observation_id":"569edd00-7888-44dc-b9b2-b9673ef152ca","resolution":{"observed_at":"2026-08-06T18:31:52.523321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01342","last_updated":"2021-06-02T17:51:05Z","snapshot_observed_at":"2026-08-05T11:00:48.512556Z","submitted_at":"2021-06-02T17:51:05Z","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01342","snapshot_observed_at":"2026-08-06T18:31:52.669644Z","title":"Bayan Bruss, and Tom Goldstein","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.669644Z"},"links":{"cited_paper":"/paper/2106.01342","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:4dd006c0b01eb6257f6ea91f283bd380d5a457e14100f58445589f2bf1252db1","observation_id":"fc3c1b1c-f961-44c5-87ca-878e3eed7da6","resolution":{"observed_at":"2026-08-06T18:31:52.669644Z","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":"2026-08-06T18:31:54.106314Z","title":"VIME: extending the success of self- and semi-supervised learning to tabular domain","venue":null,"work_id":"e6c660c8-a4e3-4c3b-a7b5-5b3653935984","year":2020},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.727338Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:46d6a183f6768744ff5fb46df56c4bfc0e70699f8d3e1c475b12628a8fe8617c","observation_id":"9df469eb-c90c-46f3-add4-9f970aba6843","resolution":{"observed_at":"2026-08-06T18:31:54.110948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2307.09249","last_updated":"2024-03-13T08:20:34Z","snapshot_observed_at":"2026-08-03T13:32:00.010393Z","submitted_at":"2023-07-18T13:28:31Z","title":"UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09249","snapshot_observed_at":"2026-08-06T18:31:52.856693Z","title":"Unitabe: A universal pretraining protocol for tabular foundation model in data science","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.856693Z"},"links":{"cited_paper":"/paper/2307.09249","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:59afb2e6ba0fc618efcd7778635914229eb5d4812498048f712839a21bb1fb78","observation_id":"352659aa-af13-4112-97d6-0da7f7d73e01","resolution":{"observed_at":"2026-08-06T18:31:52.856693Z","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":"2026-08-06T18:31:54.091941Z","title":"To predict or not to predict? proportionally masked autoencoders for tabular data imputation","venue":null,"work_id":"29bc331e-fbf6-4c2d-b337-d55e4eacb4ce","year":2025},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:52.951975Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:e0480d7d52518110c18e1bab45b15d648999def2758313cba03e486420971534","observation_id":"12b375ef-e45c-4ecc-8389-385d99cd6fb6","resolution":{"observed_at":"2026-08-06T18:31:54.096457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.076141Z","title":"Girshick","venue":null,"work_id":"ae473cee-2ff1-45d2-b5ea-7a0f495ea1ae","year":2022},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.043845Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:ee65d2d4f6c738b9d63a678466a22933bcaf382f5c47b15d52f47d62e517b718","observation_id":"bfc35636-cd9b-4f30-adce-23afd51a39ee","resolution":{"observed_at":"2026-08-06T18:31:54.081142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.060277Z","title":null,"venue":null,"work_id":"d375d8fa-5687-4d7b-9212-fef47c68ada2","year":2020},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.106476Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:1d01180ff07b21770bbe4b5966b597c8425b320c5cbc12b956296e4f8a723819","observation_id":"7a0ff76a-e030-436e-8b4a-1b3e03c037c6","resolution":{"observed_at":"2026-08-06T18:31:54.064655Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-06T18:31:53.216072Z","title":"Representation learning with contrastive predictive coding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.216072Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:ac9c7a450183fc5e0f8344e9a641ca5181d656fcd7a9dca778c1b6966cc221ef","observation_id":"9f10f50e-359a-4d2e-874a-f1e529574bbd","resolution":{"observed_at":"2026-08-06T18:31:53.216072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-06T18:31:53.330700Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.330700Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:547366338c9d4e5d35d033525623311300f4f5408322d8ab24e69d301a1927bb","observation_id":"0139db06-5cab-4e73-b59e-08bac95b78b2","resolution":{"observed_at":"2026-08-06T18:31:53.330700Z","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":"2026-08-06T18:31:54.045560Z","title":"Dash: Semi-supervised learning with dynamic thresholding","venue":null,"work_id":"b89e4ab7-65ba-4473-9ea3-e1d738a0298e","year":2021},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.447204Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:aaba556a03991087d0795e2ed1c0df03a662f0b179800026202ac52dca2662ab","observation_id":"c9a17000-cb1c-46fc-b5da-43295ca20c08","resolution":{"observed_at":"2026-08-06T18:31:54.050378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.030978Z","title":"Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling","venue":null,"work_id":"95c64841-5268-4b3e-b0c7-0a6e62778599","year":2021},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.560208Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:0c389de56bcd92195219cdc039755a61e93db1418c719feb1ae040a4241317d3","observation_id":"70425319-a36e-414b-8fd9-2d821ccc5b03","resolution":{"observed_at":"2026-08-06T18:31:54.035412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:54.015735Z","title":"Class-imbalanced semi-supervised learning with adaptive thresholding","venue":null,"work_id":"dae65ee8-d7bf-471b-a41e-203456c5c7f7","year":2022},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.564739Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:a3b01dbbfc13ede04dcc59102a5ccc4d8e4bc85a47523a23cf08b0e52da176ae","observation_id":"00edc2f9-c6d1-4763-8bdc-b3a6e82039ab","resolution":{"observed_at":"2026-08-06T18:31:54.020822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:53.999047Z","title":"Freematch: Self-adaptive thresholding for semi-supervised learning","venue":null,"work_id":"d63d9764-cf51-45b2-bac9-9cec87610b78","year":2023},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.569180Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:f20e96adf1e469c7b8f03ae8bd5cace1072ae13b69ef9d43ee6f5dd6c3b58a70","observation_id":"78b7f757-66fb-4dc9-9ef0-1d88bc248104","resolution":{"observed_at":"2026-08-06T18:31:54.003914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:53.573456Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.573456Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:08dc1d7dfa393ccf866c3b171cd97e8a5bb01ca237ef63041d74758699ee7335","observation_id":"212f3f3e-20de-4704-a1b0-f333087b8b80","resolution":{"observed_at":"2026-08-06T18:31:53.573456Z","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":"2026-08-06T18:31:53.975692Z","title":"Random forests","venue":null,"work_id":"0177e410-5bfa-40d6-b69a-3b4edc1e920c","year":2001},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.577931Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:de47b96755439231936858de006b8d55233b442fade693aa58bc55393bbea0f0","observation_id":"87827fbe-619f-4686-9633-5d15366bca7c","resolution":{"observed_at":"2026-08-06T18:31:53.979827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:53.582025Z","title":"Xgboost: A scalable tree boosting system","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.582025Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:df7416de6981f54dbd3503eb1088f5fef9395aa3480a5c4c7a8ed739dec773de","observation_id":"7acbff0c-b85b-47d2-9d31-c7aa455bca6c","resolution":{"observed_at":"2026-08-06T18:31:53.582025Z","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":"2026-08-06T18:31:53.950014Z","title":"Catboost: unbiased boosting with categorical features","venue":null,"work_id":"5dc7fdba-7530-4f95-9144-e3b431d491f0","year":2018},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.586511Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:9dff18e8661cade2e6c86d6c60a63b0dd717b2222840d2e9248b6aaefcf5d2d1","observation_id":"55009f62-68fa-437d-ac9f-881b9988c552","resolution":{"observed_at":"2026-08-06T18:31:53.954774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:53.935049Z","title":"o lkopf. Correcting sample selection bias by unlabeled data. In Bernhard Sch \\","venue":null,"work_id":"62d8fd95-f34e-444f-87f4-6f2dc4c29f92","year":2006},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.590966Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:cea5b8746aec6ba95738b6fe813ecac779bd7600c1fcd907ab33c908d0a85d08","observation_id":"4b047b3c-7236-43a3-b16c-5011099b3d1c","resolution":{"observed_at":"2026-08-06T18:31:53.939587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:53.920252Z","title":"Covariate shift by kernel mean matching","venue":null,"work_id":"6839e441-efc1-4559-9df3-8a5ebad987e6","year":2009},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.595338Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:40680939f08865cffe77a779198b66093647629047fc0c59fc55e517ca05f97f","observation_id":"4bbdec71-411c-4fe7-9529-bbb77562c561","resolution":{"observed_at":"2026-08-06T18:31:53.924886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:53.905930Z","title":"Analysis of kernel mean matching under covariate shift","venue":null,"work_id":"06f68f4f-33e8-4c57-92ca-1d8525d7416e","year":2012},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.600180Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:27046b9556491acc145a3c326c7ad87b0f66d5e1d956b99c9b3defa227a32990","observation_id":"6863a4f3-1627-482e-b12c-5a4e4d9a43a1","resolution":{"observed_at":"2026-08-06T18:31:53.910401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1505.07818","last_updated":"2016-05-26T19:56:08Z","snapshot_observed_at":"2026-07-06T04:19:15.167225Z","submitted_at":"2015-05-28T19:34:53Z","title":"Domain-Adversarial Training of Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.07818","snapshot_observed_at":"2026-08-06T18:31:53.605260Z","title":"Lempitsky","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.605260Z"},"links":{"cited_paper":"/paper/1505.07818","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:a1e8f8d1dad05aaceed0463a41fcdb472564079e9a7b79ac72f9f0a51a0a23e7","observation_id":"c54019c7-1932-4ffa-a62c-983c47045d15","resolution":{"observed_at":"2026-08-06T18:31:53.605260Z","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":"2026-08-06T18:31:53.890136Z","title":"Adversarial discriminative domain adaptation","venue":null,"work_id":"064af9d5-629c-47b1-af82-3874af3d9a87","year":2017},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.609893Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:291d0becb4d30cc8ab2e4bcfec1faf56f924bc168bd2a169e346018d0c4d00cf","observation_id":"56ac970d-992b-48c7-88db-83635a917df8","resolution":{"observed_at":"2026-08-06T18:31:53.894727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:31:53.875345Z","title":"Learning semantic representations for unsupervised domain adaptation","venue":null,"work_id":"106cc159-5678-4830-9c80-0ddd5b4297c7","year":2018},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.614063Z"},"links":{"citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:ee626fa1a78d051b4e6da602f451e8076f75d5e3cbfab4c9ee1ba7286b01d665","observation_id":"64fadffd-5061-40cb-87fe-d543d49fa6a3","resolution":{"observed_at":"2026-08-06T18:31:53.880080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1107.0740","last_updated":"2012-09-04T16:43:48Z","snapshot_observed_at":"2026-07-06T02:30:22.053026Z","submitted_at":"2011-07-04T20:34:23Z","title":"An intuitive proof of the data processing inequality","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1107.0740","snapshot_observed_at":"2026-08-06T18:31:53.619123Z","title":"An intuitive proof of the data processing inequality","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-06T18:31:53.619123Z"},"links":{"cited_paper":"/paper/1107.0740","citing_paper":"/paper/2507.08280"},"observation_digest":"sha256:1f3e4e1445e66fd07ac1ec16bb1ff9a83756034ac301f7a08907fa90e7fea306","observation_id":"3977e31e-5c4d-43af-a015-19955dea95e6","resolution":{"observed_at":"2026-08-06T18:31:53.619123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.08280","last_updated":"2025-08-14T09:57:08Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-08T01:49:58.585556Z","submitted_at":"2025-07-11T03:03:30Z","title":"MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":3,"verified_fuzzy":23},"total_outbound_references":39},"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 39 of 39 outbound references and 3 inbound Pith citation observations for arXiv:2507.08280."}