{"as_of":"2026-08-17T17:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4d8896c1c5002957c9f53d43794ccea5dfc128ed079c2f554a373547c4986918","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:59:58.247827Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.03393/citation-record","integrity":"/paper/2505.03393/integrity","json":"/paper/2505.03393/citation-record.json","paper":"/paper/2505.03393"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:59:58.015763Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.015763Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:9e6db816ec7c0ada48d57b79f30b9a331f1ef0bf58c5ccb197af13c56933c3b6","observation_id":"a5dbec24-38ce-4aaa-95e7-a95f35ad56ca","resolution":{"observed_at":"2026-08-15T23:59:58.015763Z","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":"2026-08-15T23:59:58.949767Z","title":"and Rosenthal, J","venue":null,"work_id":"b107fd4c-af08-4516-9d41-be9422e0b709","year":2020},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.022526Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:dbafa6e8f5830c38fe8bc26676eb3bbb212e542f65021a1a2acdd10372886c79","observation_id":"926d42c0-0b40-4f7c-b4e8-e3db98410f2c","resolution":{"observed_at":"2026-08-15T23:59:58.953820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.935850Z","title":"and Guestrin, C","venue":null,"work_id":"a94f58d2-7568-4d31-ba3f-15cd227b5452","year":2016},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.027924Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:fa6890f86a324bdbd82b8c85655033fd50f7d912bcaeda4168063497ee41097e","observation_id":"66c666b2-ee7a-40ea-86a0-8317feea83f7","resolution":{"observed_at":"2026-08-15T23:59:58.940694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01502","last_updated":"2024-02-02T15:36:43Z","snapshot_observed_at":"2026-08-16T22:50:31.851324Z","submitted_at":"2024-02-02T15:36:43Z","title":"Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01502","snapshot_observed_at":"2026-08-15T23:59:58.033270Z","title":"Why do random forests work? U nderstanding tree ensembles as self-regularizing adaptive smoothers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.033270Z"},"links":{"cited_paper":"/paper/2402.01502","citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:d162cab1a89442735190f3924bf409176cc4d223b554e03fd641bcd60bb0aa4f","observation_id":"1c56802d-c30b-4acb-9701-3c54326bb4f8","resolution":{"observed_at":"2026-08-15T23:59:58.033270Z","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":"2026-08-15T23:59:58.919490Z","title":"and Bj rner, N","venue":null,"work_id":"d9086d38-c064-448b-a898-342fe3f1f368","year":2008},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.038785Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:c4223ed583ed265c1a81fa4737520130b4cf705b0bac8c3d9a905dd72e7ec24b","observation_id":"ff349dfe-8184-44c5-b27f-a736f25e9e18","resolution":{"observed_at":"2026-08-15T23:59:58.925274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.905059Z","title":"Learning sparse classifiers: Continuous and mixed integer optimization perspectives","venue":null,"work_id":"92f0201e-4fb2-4520-996b-e4deadc59430","year":2021},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.044280Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:29e3604e5eb2b98b05f29e2cc62df8622e6a7628e8571b7ddfe01700f78bf7e7","observation_id":"823afaf6-4f6b-4be5-a991-e24d0fc3d44f","resolution":{"observed_at":"2026-08-15T23:59:58.909808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.889524Z","title":"Explainable machine learning challenge, 2018","venue":null,"work_id":"77827191-93b7-4497-9201-50a5ea97bf8f","year":2018},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.049767Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:fad80072dbb10b6c213edbfc96fe323b7e2df10815ccefef13310a62d5ab8046","observation_id":"b513da47-1b5e-4a63-b18c-305a190c24b1","resolution":{"observed_at":"2026-08-15T23:59:58.894362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.874452Z","title":"and Blume, J","venue":null,"work_id":"083c7c54-7ffc-4d57-8f08-52890b23fd1b","year":2020},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.055547Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:864d34f988b4a029b356b260ff13ba9e079c7bbad0d5951da110e0e9c92d44c6","observation_id":"c1a84576-c68d-42e2-979d-9affc32b2e26","resolution":{"observed_at":"2026-08-15T23:59:58.879111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.859328Z","title":"Benchmarking distribution shift in tabular data with tableshift","venue":null,"work_id":"04f4f92b-cd32-4bfa-9363-032ebac4eeb1","year":2023},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.060761Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:45e4057f7de49edf0f9b4da944ac12f1e43e4e674ec6fa423be31ba6ce5c3ee6","observation_id":"8863f147-4b6b-4e14-8167-b4eb8615d1bd","resolution":{"observed_at":"2026-08-15T23:59:58.864219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.845216Z","title":"Gurobi Optimizer Reference Manual, 2024","venue":null,"work_id":"3cdc9d82-a777-473c-b040-b6bf6e224835","year":2024},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.065858Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:9ecaebd27bf3ba02a8a8849fb142cd91a8593db4904686ae21f4963563670186","observation_id":"a2e6df61-8037-4875-a3a2-b0551ebbb9ba","resolution":{"observed_at":"2026-08-15T23:59:58.849750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.830677Z","title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","venue":null,"work_id":"b818117e-f803-41bc-8ee7-e80dcea3748e","year":2009},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.071722Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:93acb8f8e9d873c4d1fb8f8031251d8e1b303c4ec7173bd208ad3c64d71622d3","observation_id":"162c4432-0c15-481a-9edf-266f512a0499","resolution":{"observed_at":"2026-08-15T23:59:58.835758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.815999Z","title":"User's Manual for CPLEX , 2010","venue":null,"work_id":"8a02ef00-0f18-4d1f-9c23-f5756af0b628","year":2010},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.079022Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:c44ca40f95373c6903096f0d6757d906f64906e570c3bbdf1c04f43bb4268445","observation_id":"a0b17dcd-b259-439a-8765-6043e28c5bc0","resolution":{"observed_at":"2026-08-15T23:59:58.820926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.801010Z","title":"Imputation strategies under clinical presence: Impact on algorithmic fairness","venue":null,"work_id":"0d9d33eb-a13b-48b3-962c-537edcba2f5b","year":2022},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.084632Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:ac8e80d6137592b8e93aad33fa4770752fed12e1f66978c9580cefdeb2a17efb","observation_id":"e2d96552-3e30-42b2-97c7-33a3f63bac45","resolution":{"observed_at":"2026-08-15T23:59:58.806036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.786318Z","title":"L., Paulose-Ram, R., Ogden, C","venue":null,"work_id":"09d937c0-f62d-4c68-8e29-661987ce6c81","year":1999},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.091056Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:c280a8eea6b3fb942d039ba3068b91f2deccfce0eb6e41d08df15e32be13166f","observation_id":"5552226c-3959-42c0-890a-b86ae9126487","resolution":{"observed_at":"2026-08-15T23:59:58.791133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.771533Z","title":"M., Prost, N., Varoquaux, G., and Scornet, E","venue":null,"work_id":"843216aa-b76c-41e0-8652-329afaa23103","year":2024},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.096545Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:8f53f05e9136129b37f1311f944a4452d04bd7477e6ad1011b8a9f299f6e41e6","observation_id":"f17dd61d-ef8d-4f6e-894a-0a20c48d78e9","resolution":{"observed_at":"2026-08-15T23:59:58.776251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.756200Z","title":"and Bleich, J","venue":null,"work_id":"fe5129bb-3577-4409-addc-c5ab6b0b6c93","year":2015},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.104204Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:d77932d12ed7b82f7cbe6d01677657d8389fee4544cfef25d5983b0adafdc34f","observation_id":"9eff1c0e-16be-4385-8636-2afa683f49b3","resolution":{"observed_at":"2026-08-15T23:59:58.761036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-15T23:59:58.109638Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.109638Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:28b4ccb54fb376f1158719415366e5d207739824cd1add9cdec1b2b1cacdf66c","observation_id":"bc32e4f6-cba7-4e5d-90c5-b34fdb6520dc","resolution":{"observed_at":"2026-08-15T23:59:58.109638Z","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":"2026-08-15T23:59:58.742375Z","title":"Miracle: Causally-aware imputation via learning missing data mechanisms","venue":null,"work_id":"dd41a305-d2c5-44d3-ac29-ef102567734a","year":2021},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.115533Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:a8dfa5a5fc63247d217c62f040b560a80469044e4abad96e8391aa6cf8eea6fc","observation_id":"0c3be179-2719-4cd8-a023-614e06d4e075","resolution":{"observed_at":"2026-08-15T23:59:58.746554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.728058Z","title":"NeuMiss networks: Differentiable programming for supervised learning with missing values","venue":null,"work_id":"b4c7eeab-8b1a-4df6-8286-dfad316b1009","year":2020},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.120760Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:2f8a749407cfec6fe22f4b5d2f40a1ecae11d0040f4fdf85b307f02482d35869","observation_id":"cdb5afcd-ae19-4670-becd-280a3aaac283","resolution":{"observed_at":"2026-08-15T23:59:58.732977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.712198Z","title":"What’s a good imputation to predict with missing values? In Advances in Neural Information Processing Systems 34, pp.\\ 11530--11540, 2021","venue":null,"work_id":"229acf8a-7e56-489f-9198-5cf9948c916d","year":2021},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.126483Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:079252beac4937b82a0f64929b71a8614cd087317b8bde60197d31e780955477","observation_id":"670c914a-7cb8-4c1f-85d8-ff618f5c6283","resolution":{"observed_at":"2026-08-15T23:59:58.717324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.697231Z","title":"Fast sparse classification for generalized linear and additive models","venue":null,"work_id":"224ef043-0a80-41bf-bd3f-d9225afa2221","year":2022},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.131345Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:64f9d53be2d325dd01582a579bc2273c15c84b2141d5ba8e2cffa6cec186ca55","observation_id":"ab883472-aa0d-4c8b-97c1-16e6d3adcfb6","resolution":{"observed_at":"2026-08-15T23:59:58.701957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.682313Z","title":"and Chen, G","venue":null,"work_id":"55ce75de-20ad-4b1c-a0b1-fcb836ab87e6","year":2018},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.136131Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:61ca6012520d4c885035ffcee07c2058cff397e82cf99b42b912d9c5f9d39c4b","observation_id":"5efa07c6-8df1-45db-915d-ada943290568","resolution":{"observed_at":"2026-08-15T23:59:58.687129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.666487Z","title":"R-miss-tastic: A unified platform for missing values methods and workflows","venue":null,"work_id":"6a165ae2-70df-4df7-bf99-ae54c95f29d4","year":2022},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.141539Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:6af53a2c8186a70f880dcfedb0aac3f81f5d5ac1c1c9be2b9c6e10bf97471da5","observation_id":"ad04ff20-7a7d-4b1b-9533-38aac3d7b516","resolution":{"observed_at":"2026-08-15T23:59:58.671914Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.651451Z","title":"Interpretable generalized additive models for datasets with missing values","venue":null,"work_id":"7cee00f9-e6ec-436c-9ee3-6c026dd1779e","year":2024},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.146449Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:9b89edb90aef4d903a59ebea7316b10cc847b64ade11476ec487d2cabbd9de0e","observation_id":"473ffdb2-22b3-4448-9d40-893c5a59aac6","resolution":{"observed_at":"2026-08-15T23:59:58.656349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.635421Z","title":"Identifying group a streptococcal pharyngitis in children through clinical variables using machine learning","venue":null,"work_id":"1804afeb-06c0-42af-b616-fa19a6db19bb","year":2023},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.151656Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:66a01dc0731ac96ef40ad59f6ca5c3679cd87d2e1b574ae1d1f93574c8b9ada9","observation_id":"85ce7e83-bf04-4a04-8399-701423b1240d","resolution":{"observed_at":"2026-08-15T23:59:58.640970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.620217Z","title":"PyTorch : An imperative style, high-performance deep learning library","venue":null,"work_id":"4ae44877-6221-4caa-bc4f-22d50d1d059d","year":2019},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.156269Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:16c366c1902af735ffe5ccb3d5a99711db15143412295cf3460c500cf3a940ac","observation_id":"0ca544ba-a38e-4286-baba-03584ff516ba","resolution":{"observed_at":"2026-08-15T23:59:58.624953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.603086Z","title":"Probabilistic reasoning in intelligent systems: Networks of plausible inference","venue":null,"work_id":"e4f7817f-2f3e-43e1-b707-f2635fb517ef","year":2014},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.161299Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:ebbfba2ea320779d60c20f2e6832d09ce79d52a74db0cb9700814ac015eb2f12","observation_id":"e8e09d2c-3415-4a5e-b967-9ba55dd9067f","resolution":{"observed_at":"2026-08-15T23:59:58.609184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.585353Z","title":"Scikit-learn: Machine learning in Python","venue":null,"work_id":"e16d059b-2307-4c40-9863-33124b995c50","year":2011},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.166779Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:4271d66ef3b4c06c4bf6f8bc74edb417b597c6d5436e96ece61a0faddf270595","observation_id":"d9262e17-68bf-4e1a-92f4-e68946fe9983","resolution":{"observed_at":"2026-08-15T23:59:58.590916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.568966Z","title":null,"venue":null,"work_id":"b88d38ff-9462-4dad-8674-6cf54a466548","year":1986},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.171927Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:462ec0ce0645eeb0f0c2ed0697e4d732f153683c452ca8df23efe3ae77a3cf42","observation_id":"6003b67c-8a28-4c6a-bfcd-3608dbdb15f4","resolution":{"observed_at":"2026-08-15T23:59:58.573907Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.554642Z","title":null,"venue":null,"work_id":"128a74de-a852-4ce8-abd9-3cdb1d924812","year":2014},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.176332Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:9ee55f271c0c9cc4df36d60a95a6049b6b30673e56c60ae29f99f5a9071988a4","observation_id":"0d51b00b-c58f-4ef4-a3ce-94c6384b5335","resolution":{"observed_at":"2026-08-15T23:59:58.559125Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.539212Z","title":"T., Xu, G., Bandlamudi, C., Ross, D","venue":null,"work_id":"a09f1da3-08a1-4931-8038-a6a1a75d2efa","year":2018},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.180022Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:d8ec3de5f32356793acfb031a6fcbee446e2dc39d0fe601bd8dc0322520a29ee","observation_id":"e8f87041-d7c7-44c9-996f-b4e3b7b4b484","resolution":{"observed_at":"2026-08-15T23:59:58.543987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.184280Z","title":null,"venue":null,"work_id":null,"year":1976},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.184280Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:4b0fdf200cef30b8af482ecedca8e92f6005c4d2ddb30dddc831a6d7c71b5821","observation_id":"fcacc08f-7f04-424e-8594-60358bd5745b","resolution":{"observed_at":"2026-08-15T23:59:58.184280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:59:58.188333Z","title":"Interpretable machine learning: Fundamental principles and 10 grand challenges","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.188333Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:60f4815a0ab36c6d5bda699a6b09f4474248fa12d7f2d75dddeaf38ad5afe0ee","observation_id":"f01164d4-3b0f-4f30-b000-c3dc1c752d94","resolution":{"observed_at":"2026-08-15T23:59:58.188333Z","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":"2026-08-15T23:59:58.502094Z","title":"On the existence of simpler machine learning models","venue":null,"work_id":"d9fb9283-f472-4519-a6ae-f8221636ffe1","year":2022},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.192055Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:9d205d6ba11d809ba0a7678bb61bf413ee657cd766f7dffcfb98f9cbab7f79ed","observation_id":"8123e927-f9db-4afe-a97a-07de0a6fad83","resolution":{"observed_at":"2026-08-15T23:59:58.507633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.485653Z","title":"V., Sala, E., Li \\'o , P., et al","venue":null,"work_id":"43e7eb67-9d55-4e17-88c9-151e9185c2a6","year":2023},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.195719Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:0b942c8a164bdd7785fd52c662107c534a8641a10942ce0d9502ae537c9c5b6b","observation_id":"6976c604-a91c-4abe-bbc5-2ab8ad360985","resolution":{"observed_at":"2026-08-15T23:59:58.490501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.471271Z","title":"and Johansson, F","venue":null,"work_id":"22cc81e4-8e9c-4b3b-9ca3-c725d7ef22af","year":2024},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.201463Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:aa26f3013a849ae7c23ba80d57087bb6b616356d352184107d6bbfc1ce4caf22","observation_id":"313d293c-4228-4b7e-802e-2163b61f51e4","resolution":{"observed_at":"2026-08-15T23:59:58.475568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.457459Z","title":null,"venue":null,"work_id":"0b5ae9a7-0242-4c46-90f3-354e5f82e1ff","year":2023},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.205848Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:ddee74d7927889e7e075bf9fd76bc23213da469845ab5365975ea17fd7af9291","observation_id":"ca691c67-cb0b-4290-b93b-2eb05a135499","resolution":{"observed_at":"2026-08-15T23:59:58.461502Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.440759Z","title":"Regression shrinkage and selection via the Lasso","venue":null,"work_id":"bc310940-3149-4ac0-abce-0f3461c600c2","year":1996},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.210295Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:0c80a9430c803340f5e2f362b4530d335387bea3d266297f943cf2dd426988ee","observation_id":"b3f70e0c-3183-4d32-9f1e-6c7696aa95ac","resolution":{"observed_at":"2026-08-15T23:59:58.447290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.424547Z","title":"J., Nouri, D., Bossan, B., and skorch Developers","venue":null,"work_id":"cbdc5c90-1265-4f64-b664-0e99ff9cb60f","year":2017},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.214641Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:d8da53e33ed0661659801d677be1ca3e93e6b74b9eb92210c043f85c9b5bfd07","observation_id":"90a921a6-f300-42f5-b8d7-5843c7cc16b1","resolution":{"observed_at":"2026-08-15T23:59:58.429905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.408441Z","title":"E., Jones, M., and Hand, D","venue":null,"work_id":"b8145401-a626-42b8-a0ed-f5e541930c98","year":2008},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.219118Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:9d7a5e231536b5fb0ba6d3a21247fb3433a7580dbb55acaba8534765057b34e5","observation_id":"f438f4d1-6866-4741-9d2c-90cc8bf48210","resolution":{"observed_at":"2026-08-15T23:59:58.413750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.391216Z","title":"Flexible multivariate imputation by MICE","venue":null,"work_id":"c5aca76c-f37e-435e-a41f-dc806eef51a8","year":1999},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.223492Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:53a28e81d7c3b71a1c1212c92c35b63890707b140913c2e1257e7140a1df2b67","observation_id":"3e543a7a-c523-4085-ba0a-567629922c5b","resolution":{"observed_at":"2026-08-15T23:59:58.396586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.375289Z","title":"M., Halpin-Gregorio, R., and Udell, M","venue":null,"work_id":"c539942d-22c4-45c1-93e3-170994582a4d","year":2023},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.227956Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:7ff55bbde12a9463ce18cd662c72e7ed89981173a1a11db90b16121b8778859d","observation_id":"64b50885-4587-4732-8897-c035b0b10c28","resolution":{"observed_at":"2026-08-15T23:59:58.379844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.359785Z","title":"H., and Moons, K","venue":null,"work_id":"0bafdea6-6549-4103-82ad-df353db3e05c","year":2020},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.232395Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:1b34460fd211c1a107ad5a2a42815335b1a6866452bf9f24be17ad7f27a5acd3","observation_id":"f5b00165-09d5-4050-bf7d-35e607c29053","resolution":{"observed_at":"2026-08-15T23:59:58.364450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.344749Z","title":"and Zhang, Y","venue":null,"work_id":"3396980a-a967-48f8-91ba-a50d9df6df5e","year":2019},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.237226Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:20374f3fbea12514ee82ea2e017615c70024baf41e36a4f88bb0cf79440e6e88","observation_id":"54d44cee-72f7-43be-b586-a2046146946d","resolution":{"observed_at":"2026-08-15T23:59:58.349544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.328220Z","title":"W., Aisen, P","venue":null,"work_id":"2f5e0ed3-9417-474e-b301-9e8dead05e32","year":2010},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.242675Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:05988b8f8e404ce0320bc828e83e9a8780a425d973751ef3d0e4f995280cfc2c","observation_id":"96e81af7-89e9-42fe-bbab-032793a07707","resolution":{"observed_at":"2026-08-15T23:59:58.333682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:59:58.311760Z","title":"Global Health Estimates : Life expectancy and healthy life expectancy, 2021","venue":null,"work_id":"dc4d54e6-57a7-4750-903a-9b26bf95a10d","year":2021},"citing_paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-15T23:59:58.247827Z"},"links":{"citing_paper":"/paper/2505.03393"},"observation_digest":"sha256:f80247816e79217ebb008be49206bad2f90c620801972901752d4246e778c89a","observation_id":"0f128176-7740-4623-b342-a79297180c36","resolution":{"observed_at":"2026-08-15T23:59:58.317552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.03393","last_updated":"2025-05-06T10:16:35Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T22:50:51.198391Z","submitted_at":"2025-05-06T10:16:35Z","title":"Prediction Models That Learn to Avoid Missing Values"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":38},"total_outbound_references":46},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2505.03393."}