{"as_of":"2026-08-18T07:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ee173fbef4f464591b2154f5c9f400109399cafb0668ad07bc4ae18678c04f12","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T15:48:59.812014Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:58:02.823148Z","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":"2026-05-19T06:27:07.361430Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.13734","snapshot_observed_at":"2026-08-15T20:58:02.823148Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.11636","last_updated":"2025-05-16T19:00:02Z","snapshot_observed_at":"2026-08-15T20:48:31.376005Z","submitted_at":"2025-05-16T19:00:02Z","title":"Generalization Guarantees for Learning Branch-and-Cut Policies in Integer Programming","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T20:58:02.823148Z"},"links":{"cited_paper":"/paper/2501.13734","citing_paper":"/paper/2505.11636"},"observation_digest":"sha256:7434eb1d088e89aa1f3870642e452b6567d01a641d035ccd26caad065a2e12e2","observation_id":"157374ac-d899-45ec-ad6c-765fe1dafd87","resolution":{"observed_at":"2026-08-15T20:58:02.823148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"cited_work":{"arxiv_id":"2501.13734","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.13734","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","venue":null,"work_id":"9613176c-5a8e-4238-8e6f-5b23847da0d7","year":2025},"citing_paper":{"arxiv_id":"2507.05084","last_updated":"2026-04-07T16:57:57Z","snapshot_observed_at":"2026-08-17T23:38:01.984809Z","submitted_at":"2025-07-07T15:08:45Z","title":"Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-19T06:23:47.926335Z"},"links":{"cited_paper":"/paper/2501.13734","citing_paper":"/paper/2507.05084"},"observation_digest":"sha256:a2eb2b850d9b3242a0d26de90c1977a5cebbaa2ff7328bdb3b906765c89d8cca","observation_id":"a989bf67-1dba-44e2-b243-ae1c0d4b8eab","resolution":{"observed_at":"2026-05-19T06:27:07.363593Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.13734/citation-record","integrity":"/paper/2501.13734/integrity","json":"/paper/2501.13734/citation-record.json","paper":"/paper/2501.13734"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-10T15:48:59.507816Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.507816Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:8093e2b1752b56c898e3fa2759b369ff9fe24cfbc204ac9687fab8dc95481a24","observation_id":"4fd5e2c7-a47a-49e4-8428-b36fd9552bef","resolution":{"observed_at":"2026-08-10T15:48:59.507816Z","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":"2026-08-10T15:49:01.071476Z","title":"Self-improving algorithms","venue":null,"work_id":"63e27d20-3fe4-4a9a-97c8-9db9746f42e1","year":2011},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.513600Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:d5b42876327d5c4486974178d53959cae03e240d6f8dea73e0f92d236e6ddc2c","observation_id":"ac2252b5-4b6d-4f2d-a28a-dfb8355a6bed","resolution":{"observed_at":"2026-08-10T15:49:01.077228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:01.056490Z","title":"Sparse linear networks with a fixed butterfly structure: theory and practice","venue":null,"work_id":"cf5229be-74fb-46f4-91b7-bb93f85cf386","year":2021},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.517889Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:ed08e0dd1da32ac2b769f2ad5f3652e776f44c84e891d50f8c1086d7a5baa711","observation_id":"f32dacb3-a6b2-4070-b732-fea2139d4d09","resolution":{"observed_at":"2026-08-10T15:49:01.061187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:01.041097Z","title":"Neural network learning: Theoretical foundations, volume 9","venue":null,"work_id":"35c6824f-f76e-4bd3-b6d1-3c2cdbc84f99","year":1999},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.521858Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:3e07566612b1d0314b74900ed3cc501294b043901c53b79bd1ae526151d4446c","observation_id":"735b7f21-1f35-41e4-876f-5912dac9a748","resolution":{"observed_at":"2026-08-10T15:49:01.046325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:01.019804Z","title":"Designing neural network architectures using reinforcement learning","venue":null,"work_id":"8ee1bdb5-b0b6-4b05-b862-13ee49f5877c","year":2017},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.526401Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:9e1aaf943e76e4e725cd85f798343349dfaecb19bf15688b44822ff50633c33b","observation_id":"c64d8ddf-2fa7-43ea-aab3-e840b5d6b4a0","resolution":{"observed_at":"2026-08-10T15:49:01.024969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.997016Z","title":"Data-Driven Algorithm Design","venue":null,"work_id":"2b875808-80b2-4ee8-80a5-0152f1da3fec","year":2020},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.531078Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:6528ffb4bc9c78b227f5386c57ce3011061b7900e3634b24a342e174b2d6a588","observation_id":"0248a75a-3289-4d7a-8298-bf13884d2b59","resolution":{"observed_at":"2026-08-10T15:49:01.003787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.977574Z","title":"Data driven semi-supervised learning","venue":null,"work_id":"7099cdb3-f446-4908-a2c3-1206f108a99c","year":2021},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.536694Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:b66cdc48d815e8962fb7a73218f16d81b8bf0fdd1feb4c208cd9912bdaf18236","observation_id":"76a2fe61-6cab-4e2f-a487-b599c5a27c04","resolution":{"observed_at":"2026-08-10T15:49:00.984734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.957779Z","title":"Learning accurate and interpretable decision trees","venue":null,"work_id":"2b7d3bd7-12e7-4ad7-9cb4-3e9c58e4cdfb","year":2024},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.540588Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:0f7d07e4ff7e93dfafb0dd43801b5a8d85b055b0c985f916cdc46fe8bf0a5afe","observation_id":"54df198f-2562-4a46-bf02-b6738e711ad8","resolution":{"observed_at":"2026-08-10T15:49:00.964703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.930937Z","title":"Sample complexity of automated mechanism design","venue":null,"work_id":"7b1fcc26-3957-492e-84ec-4e6b320049c2","year":2016},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.543757Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:32a4c9e46b49ec7db1530ea23a22ecd85a3fdb4825973484baf0a67f4f1a76fd","observation_id":"666b61aa-fc3c-447e-970e-4ef04946884d","resolution":{"observed_at":"2026-08-10T15:49:00.937261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.546843Z","title":"Learning-theoretic foundations of algorithm configuration for combinatorial partitioning problems","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.546843Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:ad418ebcc885576800d86cb87731ae3444305ff5f16a8fc632e9d231a6e19fdd","observation_id":"ad149221-de6b-4c59-9021-e06e107d9a49","resolution":{"observed_at":"2026-08-10T15:48:59.546843Z","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":"2026-08-10T15:49:00.899186Z","title":"Learning to branch","venue":null,"work_id":"f4beca32-e81f-48a5-9cb2-ae37146d8dde","year":2018},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.550247Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:2420ca15effdba9d02687544ad4016feab4b366a895099554017acf164011112","observation_id":"563d8b00-54b9-41a2-be09-5f7ba0c61e6b","resolution":{"observed_at":"2026-08-10T15:49:00.906073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.883975Z","title":"Data-driven clustering via parameterized L loyd's families","venue":null,"work_id":"303d455a-27e2-40da-ad13-19bf81700e5f","year":2018},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.554916Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:3080d4bc05bba400691fbec783a62ebf50fe66eaeea527d7f6af383d0f2a689f","observation_id":"7a8f5ffe-70fd-41ed-a94f-a3faf96c500f","resolution":{"observed_at":"2026-08-10T15:49:00.889042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.558316Z","title":"A general theory of sample complexity for multi-item profit maximization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.558316Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:a911457f2b2633d35f9771fde84b79f77733643ddedbc1381009c99867fef73e","observation_id":"79cd4eb8-c659-45d0-ae2b-ee40783ac83e","resolution":{"observed_at":"2026-08-10T15:48:59.558316Z","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":"2026-08-10T15:49:00.857167Z","title":"Learning to link","venue":null,"work_id":"0e3eb520-5f14-4a0c-96ce-94f3a5d8336f","year":2020},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.561951Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:46db7128ad97632a87e4376261e99dbbe991726ed4a296aeb894f9761ee18f33","observation_id":"388c67e8-6b82-419f-83b0-eee04b9292a4","resolution":{"observed_at":"2026-08-10T15:49:00.862433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.843268Z","title":"Refined bounds for algorithm configuration: The knife-edge of dual class approximability","venue":null,"work_id":"daca9f75-142f-46e3-9552-c78a4146f278","year":2020},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.565826Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:8403cf8f89481130a61de22d6998e6a7b10e6656c8a8cf0af95b684bede08ea9","observation_id":"002accf5-e939-4c38-a32d-165c397ee65d","resolution":{"observed_at":"2026-08-10T15:49:00.847620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.828138Z","title":"How much data is sufficient to learn high-performing algorithms? G eneralization guarantees for data-driven algorithm design","venue":null,"work_id":"16ab238d-5eef-479a-af9d-2de530084bf3","year":2021},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.569419Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:ce260db897d062815fde101e86f16d32f044176232cba2fc2763b069fa94a88a","observation_id":"f470decf-8e19-4673-a632-7999d6e23c94","resolution":{"observed_at":"2026-08-10T15:49:00.834024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.809880Z","title":"Sample complexity of tree search configuration: Cutting planes and beyond","venue":null,"work_id":"960abec6-de03-4a80-82d7-238e31019265","year":2021},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.573119Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:3a10f0a6ced2c02fb5e2170209c0f7126b9b92354f75deeb0740ac078cebfb4b","observation_id":"9d55c0cb-f191-470f-9076-4c50212918e8","resolution":{"observed_at":"2026-08-10T15:49:00.816649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.787057Z","title":"Provably tuning the ElasticNet across instances","venue":null,"work_id":"76f13479-6d58-453b-b361-2077b0b3aad1","year":2022},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.576753Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:da99680c67cc8469d9b90580a123618d7728ebf406be0b82f4d5d40b98f1c84d","observation_id":"4eb18dbe-906b-4600-a436-194cb61bd1d2","resolution":{"observed_at":"2026-08-10T15:49:00.794818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.768186Z","title":"Structural analysis of branch-and-cut and the learnability of G omory mixed integer cuts","venue":null,"work_id":"2c2165e2-e7cf-454f-8bb4-4bf017c825cc","year":2022},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.580400Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:45b216817e1230712736790b58985407b8e8cd9d8e55aa62219313bfc887b9d9","observation_id":"7c226fec-a3d2-4166-b6c0-d318e5fe6099","resolution":{"observed_at":"2026-08-10T15:49:00.775473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.750307Z","title":"New bounds for hyperparameter tuning of regression problems across instances","venue":null,"work_id":"bb485ec4-0f09-418e-829f-48e004141145","year":2023},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.584322Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:a24db17fd569b2a3869fc81ad0524f49b9a8b7890c0cc3b8e412e75067f0d78e","observation_id":"6b0950fe-1410-4b58-bf41-6606be57c115","resolution":{"observed_at":"2026-08-10T15:49:00.755181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.04367","last_updated":"2025-05-22T03:57:47Z","snapshot_observed_at":"2026-08-16T13:20:42.830996Z","submitted_at":"2024-09-06T15:58:20Z","title":"Algorithm Configuration for Structured Pfaffian Settings","version":4},"cited_work":{"arxiv_id":"2409.04367","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.04367","snapshot_observed_at":"2026-08-10T15:48:59.891411Z","title":"Algorithm Configuration for Structured Pfaffian Settings","venue":"cs.LG","work_id":"e7af8997-785c-4c27-a336-b5a63c830faf","year":2024},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.588057Z"},"links":{"cited_paper":"/paper/2409.04367","citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:c377bc5e8a07e2edc503266cfa89aaabc7f6fef33bdb3a5e23bf58a8a43a5121","observation_id":"a7b7c9f7-0204-4974-b97c-fd9118010e0f","resolution":{"observed_at":"2026-08-10T15:48:59.898592Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.733203Z","title":"Almost linear VC dimension bounds for piecewise polynomial networks","venue":null,"work_id":"4da6f302-a982-4f0a-9df7-70db8d5c2c1d","year":1998},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.592004Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:c11a7d58bf276f7e7b07b5881f2297adacb3c6e925cb6108c4963156056f5101","observation_id":"ddff3e9c-e6b7-4243-8b30-5af265e82dd6","resolution":{"observed_at":"2026-08-10T15:49:00.740580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.713612Z","title":"Generalization bounds for data-driven numerical linear algebra","venue":null,"work_id":"2c4882d2-e02d-47f4-898e-0e20601153f5","year":2013},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.595847Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:7c53578938fb77ebcea45719f538604cae589b23aede56d340989d0a66980631","observation_id":"5699e6cb-8925-4e9b-8bc3-4e1d45acd16e","resolution":{"observed_at":"2026-08-10T15:49:00.719275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.692681Z","title":"Spectrally-normalized margin bounds for neural networks","venue":null,"work_id":"184d7aa8-e2e9-401d-bc85-5565da1f2fef","year":2017},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.599572Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:8b24c30bd2d0ec9de19540dc936b462d400359d704de290882b39385c307534e","observation_id":"41c48274-16bc-40fe-b8ba-c728e2aa7213","resolution":{"observed_at":"2026-08-10T15:49:00.697359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.677485Z","title":"Nearly-tight VC -dimension and pseudodimension bounds for piecewise linear neural networks","venue":null,"work_id":"5b5bd291-32ea-4fde-a22e-63807d5d3734","year":2019},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.604134Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:e3648cde50358e5a4a81f35f730f4bda3192231b7855d87a92544c9b5b5e794f","observation_id":"91560ea1-9e51-4dcd-8347-2c76c009381c","resolution":{"observed_at":"2026-08-10T15:49:00.682053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.607818Z","title":"Random search for hyper-parameter optimization","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.607818Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:d35fdcad1e527b831e6903d7a90b6945ee276912759719cea6e3fa4e6d74a206","observation_id":"78a22b4c-197c-4789-8ca5-f8886ad4c677","resolution":{"observed_at":"2026-08-10T15:48:59.607818Z","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":"2026-08-10T15:49:00.648074Z","title":"Algorithms for hyper-parameter optimization","venue":null,"work_id":"0be2b4f5-47f2-421f-b963-758920811683","year":2011},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.611651Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:79fddded3a54fac67d100f8ff144e0f3c41aa5f95b6053b575633a2e1420249f","observation_id":"75287953-c9e7-4ad4-9c43-df4435672142","resolution":{"observed_at":"2026-08-10T15:49:00.653802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.625799Z","title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures","venue":null,"work_id":"01cec48b-6d38-4dfc-bab7-a1cd9ba59319","year":2013},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.615904Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:8b932224c6eaa07351128b9278201927c4fccb3c1210dc42eb6eb451ed67641a","observation_id":"bd2881a0-1975-47ad-a1ec-f8b5264569d5","resolution":{"observed_at":"2026-08-10T15:49:00.634167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.606382Z","title":"Learning from labeled and unlabeled data using graph mincuts","venue":null,"work_id":"ba9ba2e2-40bf-4657-a672-a6be95b0038d","year":2001},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.619533Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:3cb3ea4f0b479e79cafced1e1d124a3e1d14ec5fc188db1ce23874193158afd4","observation_id":"6985efe1-a6da-48e0-9c5e-a2af5c56cd61","resolution":{"observed_at":"2026-08-10T15:49:00.610465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.588597Z","title":"Advanced calculus","venue":null,"work_id":"d0fc6eb1-1ffe-4c28-8596-16e3488d6d12","year":2003},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.623296Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:4c6c8ae52de07b442d327580dda75223f44a4d0ad447551dc83ef73c09ca128a","observation_id":"6aacbe5d-ac83-49c3-b8e0-a2bbd8e0fe1c","resolution":{"observed_at":"2026-08-10T15:49:00.594193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-10T15:48:59.635483Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.635483Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:4d93b35df11edadcd5796954e7ea16a1e823e45967ad642cbe9e81b33b43bbcf","observation_id":"3dc9a4f3-dd3d-4c0b-ba09-9e195a0bb595","resolution":{"observed_at":"2026-08-10T15:48:59.635483Z","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":"2026-08-10T15:49:00.564233Z","title":"Nas-bench-201: Extending the scope of reproducible neural architecture search","venue":null,"work_id":"ede5159e-8feb-402e-abe7-6800db66af02","year":2020},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.641281Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:309b294374421d1838862329b4cacd29e59c05d6914a6bbfe8930ef6e62fd285","observation_id":"95471c8c-b842-4570-8a7b-235a186577dd","resolution":{"observed_at":"2026-08-10T15:49:00.574948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.546434Z","title":"Simple and efficient architecture search for CNN s","venue":null,"work_id":"ab1f4d29-511c-46d1-a7c6-d8a2a8dbfdda","year":2017},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.645881Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:acde8efaf918fd623e2b000fbe4206ed806fc062355fd929bac3414f98e96b91","observation_id":"e15403c5-39b2-42e7-86b4-325a2346bdab","resolution":{"observed_at":"2026-08-10T15:49:00.550531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.650150Z","title":"Neural architecture search: A survey","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.650150Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:2688b880b33f9bc73f9bdec31110be938a660c7f0dcc21fd4c7a4a52f7d025ec","observation_id":"5bf709e9-b166-4572-ba84-4798ea8192e4","resolution":{"observed_at":"2026-08-10T15:48:59.650150Z","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":"2026-08-10T15:49:00.522173Z","title":"Neural message passing for quantum chemistry","venue":null,"work_id":"e02d30dc-6abf-43a2-aeef-1979b8f8e916","year":2017},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.654011Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:f407cc3c9da42b8527065eec4811bb09c88e14e812fd01a4a3afffa7b37c8dfe","observation_id":"ca50dd9f-fe42-447d-81cc-592bfaf42755","resolution":{"observed_at":"2026-08-10T15:49:00.526578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.503626Z","title":"A PAC approach to application-specific algorithm selection","venue":null,"work_id":"c569927e-699a-413b-8023-edb697b1c933","year":2016},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.658068Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:8a362c754f697558c977b35c29ec1270da60dc8545eca85a061473a4103b5a4b","observation_id":"f07450c5-b2cf-49eb-8ee6-9378143a0284","resolution":{"observed_at":"2026-08-10T15:49:00.508270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.488087Z","title":"Data-driven algorithm design","venue":null,"work_id":"0fe68c58-0f95-4b34-a234-4adf7ab58382","year":2020},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.661612Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:2b6218a80c25da381f442ccb3ac8b5bbb0eaee36d32ad3a136cbcc574a0f29b7","observation_id":"dfd8170d-0aeb-43e7-a4ae-8a84c4937b57","resolution":{"observed_at":"2026-08-10T15:49:00.494375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.469061Z","title":"Hyperparameter optimization: A spectral approach","venue":null,"work_id":"f06b36f7-ed35-4649-a9b4-2fd2060f8c60","year":2018},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.665266Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:856f10c0de4532f190ae84d43509cde04cd4e41146a2e555beade4bf3488eb3c","observation_id":"d4790a7c-1fa2-4e96-b2e9-b281aed0cdb3","resolution":{"observed_at":"2026-08-10T15:49:00.474946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.668750Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.668750Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:3421b4ac78b254f8bd59564c8a9b99fafd5af6367ede1f2b4a7754e0b449390a","observation_id":"e5daabb8-8c67-4ef6-b27c-07e90df80b05","resolution":{"observed_at":"2026-08-10T15:48:59.668750Z","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":"2026-08-10T15:48:59.672298Z","title":"Sequential model-based optimization for general algorithm configuration","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.672298Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:d5dec9d46d251501d584dbfc78c47f28d244080d4bc12e7943a75a452a84c365","observation_id":"01130de3-d321-44b0-91bb-25dc873f38c1","resolution":{"observed_at":"2026-08-10T15:48:59.672298Z","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":"2026-08-10T15:49:00.420339Z","title":"Learning-based low-rank approximations","venue":null,"work_id":"47eb80ea-0031-4648-a88d-e6a3e026d906","year":2019},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.675586Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:ce2014dcb98501b5c008c78d9305815d5a6c8c3fe2a2667474fa22457e2cf2f6","observation_id":"64801833-5a10-434f-9bb1-20a45bca1fa1","resolution":{"observed_at":"2026-08-10T15:49:00.429496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.400176Z","title":"Polynomial bounds for VC dimension of sigmoidal and general P faffian neural networks","venue":null,"work_id":"4cf1447f-20c4-414d-ad6f-9cd66ce69c67","year":1997},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.679308Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:6f63c9605f2017da54fe8de2ef480ed85b9351242728d067f8cd405d36446702","observation_id":"15b4d3c1-489c-4c69-ac16-0c8c4e0ae831","resolution":{"observed_at":"2026-08-10T15:49:00.406008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.381746Z","title":"Learning to relax: Setting solver parameters across a sequence of linear system instances","venue":null,"work_id":"e255a33b-9261-4278-9a8f-4f30d9ced1a1","year":2024},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.683350Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:9e538244fd7d5c7fec9100781e4514f9c27207ca169fd9fa6430b9bd8ba89f99","observation_id":"cfb885b6-12b8-49e3-b7b2-deb4e22cd047","resolution":{"observed_at":"2026-08-10T15:49:00.387734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.364538Z","title":"Fewnomials, volume 88","venue":null,"work_id":"1d2241da-5962-4423-b8ea-a1b6de1230a0","year":1991},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.687821Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:26288a46646d0000bad2409530a521014abad13e1147c5f7b23fc1c3a3ca0d2a","observation_id":"d4e30e40-2676-492d-bced-17264f2f489c","resolution":{"observed_at":"2026-08-10T15:49:00.370004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.691841Z","title":"Kipf and Max Welling","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.691841Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:bc6da6c43d0d42917760d0d0e5864ecb8740c16abac5b651dae6726aa6305718","observation_id":"83d4c457-08ac-46af-8697-2134aa594615","resolution":{"observed_at":"2026-08-10T15:48:59.691841Z","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":"2026-08-10T15:49:00.339293Z","title":"Geometry-aware gradient algorithms for neural architecture search","venue":null,"work_id":"631883ba-8a93-4608-8458-f46bbd90b675","year":2021},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.695847Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:f4752f5b65a0adfa95c6e3c2bda2e022021a2adb8c51b3fef117e6bcccd6be94","observation_id":"7aace2aa-18b4-4ffa-a747-dd9a64f8005f","resolution":{"observed_at":"2026-08-10T15:49:00.344431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.699580Z","title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.699580Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:310f0d3a3c5571b2b99722cc827cfab36c96613b184dfbbf1f6afaf7c3684a1b","observation_id":"d43bc131-28dc-4945-a177-098b3d9d44b3","resolution":{"observed_at":"2026-08-10T15:48:59.699580Z","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":"2026-08-10T15:49:00.314160Z","title":"Learning the positions in countsketch","venue":null,"work_id":"276a21f6-e826-419b-8bff-981a85c91aec","year":2023},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.703370Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:f536e0e62bb43f7c3b3c9857ef6468835e4803d175715e6e1e5eec15ef36ce0f","observation_id":"cf0b1ce0-3b5e-40c5-b508-74955cc53b27","resolution":{"observed_at":"2026-08-10T15:49:00.318800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.298966Z","title":"Progressive neural architecture search","venue":null,"work_id":"2251cc04-b0cb-4aa1-a9ed-3ada05f2c042","year":2018},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.707440Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:fa2da02a3043f4bb4a598f67a09d18712287e7c3b1a03671763ff266e4cd35c5","observation_id":"29f03b06-f32f-4c1b-af52-11b8e24a8adc","resolution":{"observed_at":"2026-08-10T15:49:00.303586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.283383Z","title":"DARTS : Differentiable architecture search","venue":null,"work_id":"8dacdd15-a22e-4029-8d3e-7ab5563596b1","year":2019},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.711707Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:1574d3aa5d2333df5abf8f2cae5451111ea4b62f6763e05a3bbea8fa390bf1aa","observation_id":"366f1c86-0759-485e-84f5-43248c5bce0a","resolution":{"observed_at":"2026-08-10T15:49:00.288612Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.264473Z","title":"Learning algebraic multigrid using graph neural networks","venue":null,"work_id":"384147c3-efd6-4ae4-bda6-8e25d8c897c0","year":2020},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.715562Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:bcdd763f52f9406e61c9c3eb567b3284986e3b1672f7563727b8c63c2c25afb4","observation_id":"055fbc13-1631-4179-9690-ba567394026d","resolution":{"observed_at":"2026-08-10T15:49:00.270932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.248848Z","title":"Neural nets with superlinear VC -dimension","venue":null,"work_id":"fe4230d1-7f05-4d44-b4a7-198671e381c2","year":1994},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.719828Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:b93e406edc9ced2d3561528ef8c89f489ce6c606195c5a98ed9937c20e094db4","observation_id":"9c413f68-278a-400e-b2ce-890720611674","resolution":{"observed_at":"2026-08-10T15:49:00.254313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.230194Z","title":"NAS-Bench-Suite: NAS evaluation is (now) surprisingly easy","venue":null,"work_id":"47810770-687b-41a8-8d97-7b61dd9556c4","year":2022},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.723507Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:f14eb0bbb5d72f2f1c7d5e2ee25c316e7bb67958b76d023efe8bc28c03aab16b","observation_id":"0b548c92-8ed0-4b01-a4d6-c08204e8d01f","resolution":{"observed_at":"2026-08-10T15:49:00.237591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.727468Z","title":"Towards automatically-tuned neural networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.727468Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:d774c57af51dc59fdce04214f0dfa72ba74049468183939d05fb5f4d936466b7","observation_id":"9a4bce2c-7e46-46a7-a721-85a540e7ba49","resolution":{"observed_at":"2026-08-10T15:48:59.727468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.08792","last_updated":"2017-04-28T02:48:38Z","snapshot_observed_at":"2026-08-14T21:04:09.797341Z","submitted_at":"2017-04-28T02:48:38Z","title":"DeepArchitect: Automatically Designing and Training Deep Architectures","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.08792","snapshot_observed_at":"2026-08-10T15:48:59.731476Z","title":"Deeparchitect: Automatically designing and training deep architectures","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.731476Z"},"links":{"cited_paper":"/paper/1704.08792","citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:ed774f03ee6e884b24b76f10a16862f3fbf4a0b37873147dfe880c2937d1d404","observation_id":"1df5c196-c275-464c-99cf-b40db4c45c44","resolution":{"observed_at":"2026-08-10T15:48:59.731476Z","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":"2026-08-10T15:49:00.198187Z","title":"Efficient neural architecture search via parameters sharing","venue":null,"work_id":"2667155c-2252-4105-abed-2a9af945c75a","year":2018},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.736085Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:5fbdc4b807b00813b121c3d8c343d6f0f857902c9d8d1748775ac13d421fb520","observation_id":"156ce9da-784f-4901-844c-a3b352477a21","resolution":{"observed_at":"2026-08-10T15:49:00.208482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.182619Z","title":"Convergence of stochastic processes","venue":null,"work_id":"959b0cf6-1c10-49ca-8193-d5890117ec52","year":2012},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.739808Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:3ca315a34e416d148703eecc00f5e29df5f2aa822af4868341ee221e74ab1ed1","observation_id":"efd91783-721a-4c2b-b4f3-083e6f8a2eeb","resolution":{"observed_at":"2026-08-10T15:49:00.187985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.05941","last_updated":"2017-10-27T17:45:21Z","snapshot_observed_at":"2026-08-08T18:23:31.977872Z","submitted_at":"2017-10-16T18:05:45Z","title":"Searching for Activation Functions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.05941","snapshot_observed_at":"2026-08-10T15:48:59.743447Z","title":"Searching for activation functions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.743447Z"},"links":{"cited_paper":"/paper/1710.05941","citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:b83fff84fc85c0a6323e2a93a0e9352c4ff061a5a9d8c0fe4f8dda5b2beadc92","observation_id":"3a083b08-7eec-4796-b903-cd55b4cb86aa","resolution":{"observed_at":"2026-08-10T15:48:59.743447Z","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":"2026-08-10T15:49:00.167677Z","title":"Introduction to differential geometry","venue":null,"work_id":"7382915f-a957-4835-ad9a-626c1ac09ebd","year":2022},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.748049Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:64a78fb030087cfec447e9d197aa8bad714bb9724da6d87851bd24cdc4fbfa6c","observation_id":"8eef666b-4515-44f4-b3b1-e8069730d141","resolution":{"observed_at":"2026-08-10T15:49:00.172485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.152083Z","title":"Lagrange multipliers and optimality","venue":null,"work_id":"426b69c5-c863-4919-bf5e-ee41b608163a","year":1993},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.752002Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:4e0ebc5e937a60a4d7e280db8223b9f4a16a5fef1628c007318219cbcfaa8b9f","observation_id":"7c087a87-a989-4ad0-b7cd-a5fe1d4fe818","resolution":{"observed_at":"2026-08-10T15:49:00.157264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.755964Z","title":"Variational analysis, volume 317","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.755964Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:69d0402b1203290a1d764d297e011d8a340ac7890e85b919ebb60e7161a244b6","observation_id":"a944008e-0cfd-46e7-8bff-5558bee8739e","resolution":{"observed_at":"2026-08-10T15:48:59.755964Z","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":"2026-08-10T15:48:59.759725Z","title":"On the density of families of sets","venue":null,"work_id":null,"year":1972},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.759725Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:9aa6fd3e38f6ff4ff1b18c8193ba192d27a00015c7e22847db9976d3a676f326","observation_id":"45e9ca85-9460-49c4-bb49-2b94ffabb97c","resolution":{"observed_at":"2026-08-10T15:48:59.759725Z","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":"2026-08-10T15:49:00.111303Z","title":"Understanding machine learning: From theory to algorithms","venue":null,"work_id":"8807d9b6-2b5e-4a51-a9f5-9548cbb3b700","year":2014},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.763480Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:79d9f8162c5100c970db4e6865c92c315d982956f0093d85802eaf68e6004760","observation_id":"470fc8cc-310f-4e2e-867c-2de5c92f8ebf","resolution":{"observed_at":"2026-08-10T15:49:00.120569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.767174Z","title":"Efficiently learning the graph for semi-supervised learning","venue":null,"work_id":null,"year":1900},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.767174Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:fa35a784f2c1f560e5cc68469a3a9c41d8876d9f0d995317755360477a7dbbab","observation_id":"0af78866-fe78-4d48-b2ff-96b4fe1baf03","resolution":{"observed_at":"2026-08-10T15:48:59.767174Z","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":"2026-08-10T15:49:00.078896Z","title":"Practical B ayesian optimization of machine learning algorithms","venue":null,"work_id":"4076d184-3ace-4494-9a75-f2d8eda0a08f","year":2012},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.771706Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:d0da047cd27c0f481d7972ac154817edeb6e70ffc8387dec7ec75c6b09ed307d","observation_id":"d909190b-ee7d-4314-954b-6c2b9c77f45a","resolution":{"observed_at":"2026-08-10T15:49:00.085563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.061800Z","title":"Scalable B ayesian optimization using deep neural networks","venue":null,"work_id":"feb88438-bf67-43df-b2fc-db0a9db51497","year":2015},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.775551Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:42568e9a50b0611fb869063f2240f4bd657f04c1238745676fe2669a20701db8","observation_id":"71cb00b4-fefd-4c9a-8cfe-420ab0cdf3ad","resolution":{"observed_at":"2026-08-10T15:49:00.068335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:49:00.044482Z","title":"Graph attention networks","venue":null,"work_id":"d4c9960a-8527-45ac-86b1-a60fa2709079","year":2018},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.779577Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:8f55c6249900c3302200b1a1eb26bba87045c7435f0a90d5fc2add6e251d1322","observation_id":"1af26f61-9029-498f-b023-8151785cb1ec","resolution":{"observed_at":"2026-08-10T15:49:00.048770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.784355Z","title":"High-dimensional statistics: A non-asymptotic viewpoint, volume 48","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.784355Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:7c00ecd167c7ad85aafb38e30aab5767ea1e7a94125edf3f3f78fad0a6069668","observation_id":"95a12886-dc91-4f0e-96be-0522e89ca11a","resolution":{"observed_at":"2026-08-10T15:48:59.784355Z","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":"2026-08-10T15:49:00.005100Z","title":"Lower bounds for approximation by nonlinear manifolds","venue":null,"work_id":"243220b2-9fed-409f-a4a8-6dc0cd49d0fe","year":1968},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.788565Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:f61902d8cad9dbe80e5e37e3536489312030a3c22519b54b01b375477e23ee01","observation_id":"294bc960-033a-413f-aa64-0c35144c6f6e","resolution":{"observed_at":"2026-08-10T15:49:00.011557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.987894Z","title":"Bananas: B ayesian optimization with neural architectures for neural architecture search","venue":null,"work_id":"e14c3f2f-7e4c-411f-acb0-65bb321170ad","year":2021},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.792695Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:542a6e23cbafebe331296b336ffee6dda7f08334f9c51805898f8be7abdcec4a","observation_id":"21edacf1-6caf-4f1b-84c5-e680f0fea567","resolution":{"observed_at":"2026-08-10T15:48:59.992376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.975196Z","title":"Simplifying graph convolutional networks","venue":null,"work_id":"8e4bb6f2-bf8d-431c-8bc4-03a5b439c7dd","year":2019},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.796561Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:71e440bd86f9d5832c4dc87938861a1df5d77a3c5c058b0e857f38fcf3f76776","observation_id":"d0163511-dba1-4bb5-87e0-1ca922882f44","resolution":{"observed_at":"2026-08-10T15:48:59.979205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.961428Z","title":"Learning with local and global consistency","venue":null,"work_id":"d106a654-8258-47cb-9b13-7cb6560a3888","year":2003},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.800256Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:493408db295b519f5da9e236cdf2a17dcc8f83ca5e0c7896c560355d568c005e","observation_id":"f5736f3c-826e-4214-85e6-aeaee3041901","resolution":{"observed_at":"2026-08-10T15:48:59.966078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.948847Z","title":"Semi-supervised learning with graphs","venue":null,"work_id":"ae92c6c1-460b-4c18-9910-20d7acd68c92","year":2005},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.803932Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:7b413c3fa7dfacfd1e464865c34e3e2257ded73bcbe61070155951c40bf2dd31","observation_id":"6a8b2f28-7218-4d32-93dd-025a5cb015fe","resolution":{"observed_at":"2026-08-10T15:48:59.952892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.935589Z","title":"Semi-supervised learning using G aussian fields and harmonic functions","venue":null,"work_id":"bed81afa-c433-4da1-9c2d-a95ef829f86f","year":2003},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.807693Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:0aba9f6bcea85fd8f45c445773c0f67869b5832786b981e79f7ac9b87f3f0751","observation_id":"3e670abb-b847-4472-ab09-b33f98275c7c","resolution":{"observed_at":"2026-08-10T15:48:59.940947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-10T15:48:59.922103Z","title":"Neural architecture search with reinforcement learning","venue":null,"work_id":"318a7c03-7b18-4469-8c38-f24e9a967922","year":2017},"citing_paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function","version":4},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-10T15:48:59.812014Z"},"links":{"citing_paper":"/paper/2501.13734"},"observation_digest":"sha256:4efd1c8396afae5a7f9611de19ddda470d28a3b56921ca24476eed1c8e0a30b5","observation_id":"94d702bf-b588-4727-8efe-4c9db3690726","resolution":{"observed_at":"2026-08-10T15:48:59.926431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.13734","last_updated":"2025-04-29T18:05:37Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T02:16:23.098708Z","submitted_at":"2025-01-23T15:10:51Z","title":"Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":1,"verified_fuzzy":57},"total_outbound_references":75},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 2 inbound Pith citation observations for arXiv:2501.13734."}