{"as_of":"2026-08-06T06:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7132e5bc3ded37c710513e31b2768d06059f22538cced7cc92823558a1c727b9","coverage":[{"denominator":145,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-24T02:38:47.015471Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T00:55:30.514797Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09532","snapshot_observed_at":"2026-08-02T00:55:30.514797Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems.arXiv preprint arXiv:2403.09532, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.14862","last_updated":"2026-07-16T11:36:00Z","snapshot_observed_at":"2026-08-02T00:55:25.616571Z","submitted_at":"2026-07-16T11:36:00Z","title":"Tamed Stochastic Gradient Hamiltonian Monte Carlo","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-02T00:55:30.514797Z"},"links":{"cited_paper":"/paper/2403.09532","citing_paper":"/paper/2607.14862"},"observation_digest":"sha256:1f5956e70c30e9592fa3b3d4565dcba5d2537f593d9556be47a92067d4cc386a","observation_id":"6ed34e37-09a6-421c-ac7f-74322eb974e2","resolution":{"observed_at":"2026-08-02T00:55:30.514797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2403.09532/citation-record","integrity":"/paper/2403.09532/integrity","json":"/paper/2403.09532/citation-record.json","paper":"/paper/2403.09532"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Adageo: Adaptive geometric learning for optimization and sampling","venue":null,"work_id":"5571aa1e-1e8d-4c0c-83d3-78523dea7925","year":2018},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:6082bab30f54763d50545c4b5446fd1ceb9819e3e8b51a23d0243a45d1a6bf0f","observation_id":"442575f4-d49e-4537-b3f1-2e7c62c4d4b8","resolution":{"observed_at":"2026-05-24T02:45:57.369292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A model-free version of the fundamental theorem of asset pricing and the super-replication theorem.Mathemati- cal Finance, 26(2):233–251","venue":null,"work_id":"8fe5f34b-24bb-4a44-abd2-dcd64d9b7ec0","year":2016},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:e67ddd983d0958fd7ffee632025f0871cb84213390a5eaef9dc305224250405b","observation_id":"ca724c71-7478-4c38-a9c2-315b2692a6f0","resolution":{"observed_at":"2026-05-24T02:45:57.364809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Bayesian posterior sampling via stochastic gradient fisher scoring","venue":null,"work_id":"cb36410b-e5e9-4458-8d31-f57d126b2113","year":2012},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:5f25822e8e691ec729d0269734bf18fb9ad90b0fcdd4d06099a075b1091d9279","observation_id":"8b3a4101-b290-492c-83cc-dc78eb52d15d","resolution":{"observed_at":"2026-05-24T02:45:57.361880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Stochastic gradient mcmc for state space models.SIAM Journal on Mathematics of Data Science, 1(3):555–587","venue":null,"work_id":"7a715ab4-6dbe-43e1-b34b-68d882916e9a","year":2019},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:22b71a2595bd232e8bfec46afeb3a0dac95afc57d11436060c630ac0a8c0cc15","observation_id":"c8498696-ac28-4c2f-bcaa-08ec5277786c","resolution":{"observed_at":"2026-05-24T02:45:57.358951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Physics-informed information field theory for modeling physical systems with uncertainty quantification.Journal of Computational Physics, 486:112100","venue":null,"work_id":"2eaa2fd7-11a0-42db-85d6-0f3e210df621","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:a3f9b820e24d07f6c7b6a74f7b5c85ed8d6b1842da7680e49c7b7306669391e0","observation_id":"f32409bf-6abd-49d7-9254-79505cd6fff9","resolution":{"observed_at":"2026-05-24T02:45:57.352960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Efficient optimal transport algorithm by accelerated gradient descent","venue":null,"work_id":"1381d6f2-3ecd-45b0-b91a-8002b3aaa573","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:de48a696684c1925ada5a569bf85e353fab4e91224fb9514feed5482953a7b95","observation_id":"29868b49-ada8-4bb7-926f-a269d4c1bee5","resolution":{"observed_at":"2026-05-24T02:45:57.349924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Wasserstein distributionally robust estimation in high dimensions: Performance analysis and optimal hyperparameter tuning","venue":null,"work_id":"c7972c1c-512c-4ece-a014-a7a4fc6c537b","year":null},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:09a8611e495b1b6c2c1c71f3e6665492e06d26122dc0cbb2c61ce938da099719","observation_id":"7cbdc3e2-2cd6-4249-a3c3-13465c526195","resolution":{"observed_at":"2026-05-24T02:45:57.347025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.00343","last_updated":"2023-09-07T17:18:21Z","snapshot_observed_at":"2026-08-05T17:29:34.547123Z","submitted_at":"2022-04-30T21:09:19Z","title":"Distributional Uncertainty Propagation via Optimal Transport","version":2},"cited_work":{"arxiv_id":"2205.00343","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.00343","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Distributional uncertainty propagation via optimal transport","venue":null,"work_id":"8597c3cf-b1f4-463e-9542-d18e61c269dd","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2205.00343","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:348b326db237f9cc09bebe6a5573b357a87ed26ee84ef26cef70dac536932967","observation_id":"0b7ea70c-eae0-4913-bd41-e8b64dafd310","resolution":{"observed_at":"2026-05-24T02:43:47.745505Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Deep learning and optimisation for quality of service modelling","venue":null,"work_id":"6fb0a04b-d465-424e-b113-d17494644a19","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:ca9debadd138c87822bc060a0a8488e4d81c2c71159d0f5b910e0438f4f05907","observation_id":"cfe5cc06-7938-454e-9473-b2d93380a035","resolution":{"observed_at":"2026-05-24T02:45:57.343509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case.Bernoulli, 27(1):1–33","venue":null,"work_id":"bc26d769-ffdc-483a-b5ad-250ddfc59fb5","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:e41a6040b3975849c8dd15b9dd9f85d85311523c1a6d21a84221105a1feec723","observation_id":"3cf7fb7b-333d-4592-b91e-4ab423e5d023","resolution":{"observed_at":"2026-05-24T02:45:57.340392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Sensitivity of multiperiod optimization problems with respect to the adapted Wasserstein distance.SIAM J","venue":null,"work_id":"6ec83c49-04a9-4231-b636-a4e9bae4c748","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:50d7957448e0d188267e24ffb46fe1429b34ace9ecfa63bcda24f0b2351e8340","observation_id":"dbd85102-4ac1-4906-a363-b8a8b4bdff09","resolution":{"observed_at":"2026-05-24T02:45:57.337296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.10186","last_updated":"2019-03-06T13:21:11Z","snapshot_observed_at":"2026-07-06T05:49:15.272401Z","submitted_at":"2017-06-30T13:22:11Z","title":"Computational aspects of robust optimized certainty equivalents and option pricing","version":3},"cited_work":{"arxiv_id":"1706.10186","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1706.10186","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Computational aspects of robust optimized certainty equivalents.Preprint","venue":null,"work_id":"d373d289-38ca-41f4-a651-0f2fa11d7061","year":2017},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/1706.10186","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:7b8a31e2b5ea5d70fce868788307b40cd3cc452410f05dd7cedba1b36c0f071e","observation_id":"96f1c384-c16b-47e1-8fb5-83409208a907","resolution":{"observed_at":"2026-05-24T02:43:47.733875Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Sensitivity analysis of Wasserstein distributionally robust optimization problems.Proc","venue":null,"work_id":"64e984e5-a44b-4a6a-9e86-183ff0d6f21f","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:d084b1149f34666e12f7db6c642c9fbc4b8897672cde1f523a1da1981acb9278","observation_id":"d9f536d2-fa1c-468e-aa25-43c342688b75","resolution":{"observed_at":"2026-05-24T02:45:57.334242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Duality theory for robust utility maximisation","venue":null,"work_id":"a8a88d73-2ede-4119-934e-05b284cc1b86","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:4a64020a2cce2eb323e0f0b3ee9752f0826cb9bdf22d4f388ebdcacd002a7d98","observation_id":"6e8edb64-502f-49a5-b9af-02aca4433faf","resolution":{"observed_at":"2026-05-24T02:45:57.331211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11248","last_updated":"2025-02-24T17:41:46Z","snapshot_observed_at":"2026-07-06T16:49:33.776407Z","submitted_at":"2023-11-19T06:26:58Z","title":"Sensitivity of robust optimization problems under drift and volatility uncertainty","version":2},"cited_work":{"arxiv_id":"2311.11248","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.11248","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bartl, A","venue":null,"work_id":"4e03fe88-1318-458c-a9c9-b25a007d72fb","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2311.11248","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:9eab82e658a7e6174e07c8e9b39d71c859b6f8a27ba3a6cbefc77cd8adc59916","observation_id":"cd940ec2-8721-4c2f-b320-3e99f44ca5de","resolution":{"observed_at":"2026-05-24T02:43:47.605849Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.11910","last_updated":"2026-04-14T14:10:18Z","snapshot_observed_at":"2026-07-06T17:46:25.142387Z","submitted_at":"2024-03-18T16:11:43Z","title":"Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis","version":3},"cited_work":{"arxiv_id":"2403.11910","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.11910","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis","venue":"math.NA","work_id":"7101b4ad-61eb-402f-b1d8-55a3d757b646","year":2024},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2403.11910","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:1927ef82e10ffb0d36f781ddd34472a4512af250635265ce5a4ce0fe814a28f1","observation_id":"5603756b-2520-4352-bf6f-5a9f7f0197c3","resolution":{"observed_at":"2026-05-24T02:43:47.622122Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Data-driven non-parametric robust control under dependence uncertainty","venue":null,"work_id":"16901956-c1f8-4650-a9e6-01e323bebb6b","year":2024},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:c43d9ebb6f06df38fea1ae185aee029e950b502906b63e26bcc88bca7f65983d","observation_id":"f7fb8455-cab3-43f8-9c50-76fe436b6133","resolution":{"observed_at":"2026-05-24T02:45:57.328382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Equilibria under knightian price uncertainty.Econometrica, 87 (1):37–64","venue":null,"work_id":"e8bafbef-3b0d-4a35-80c3-6686d74ec0cf","year":2019},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:ee4150c5fd9ab4af66167132ad926be0151e33fc869ffe6d1e17c8dc25e6b588","observation_id":"d61ade25-ce69-4aab-ad38-c65f1c182606","resolution":{"observed_at":"2026-05-24T02:45:57.325256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Estimation and uncertainty quantification for the output from quantum simulators.Foundations of Data Science, 1(2):157–176","venue":null,"work_id":"0a827b34-448e-4c5b-8e23-11e60b08e11b","year":2019},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:8b0189b212ddaba225f4c92e0242d707cc2dcca5856bd801432ef9310f030e5b","observation_id":"757c4129-f310-481a-8aae-bcb813310d68","resolution":{"observed_at":"2026-05-24T02:45:57.322312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust distortion risk measures","venue":null,"work_id":"78e54293-9a54-4caf-b0dc-a2ee363b20ae","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:e32d8bef71b0424f97c316bbc7bd2f670ac9ca3222a41d0d2b41bb78b60858bd","observation_id":"56cd86a6-984d-418f-9774-091021934f65","resolution":{"observed_at":"2026-05-24T02:45:57.319171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Models for minimax stochastic linear optimization problems with risk aversion.Mathematics of Operations Research, 35(3):580–602","venue":null,"work_id":"e9eef165-010d-441c-a732-162cf0f7a4bd","year":2010},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:6837dfddb38e6e445dd46be17f0d105ca3da49ad1f31abcf3442d76a0739e08c","observation_id":"f7b53f1d-2d69-4e99-97e4-8e1d9c7f8f93","resolution":{"observed_at":"2026-05-24T02:45:57.315785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Scaling up dynamic topic models","venue":null,"work_id":"40b2cb70-cdae-44de-9a49-13be7249c708","year":2016},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:1405f91c7f984448b6874d9774dc6e6b2136a4c6ffd288161b1c22d06bce40c4","observation_id":"ceab8a9c-47ac-4095-8823-d33a35367885","resolution":{"observed_at":"2026-05-24T02:45:57.312568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Multiple-priors optimal investment in discrete time for unbounded utility function.The Annals of Applied Probability, 28(3):1856–1892","venue":null,"work_id":"13138336-d21e-4795-8c0a-1d13b2195450","year":2018},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:c4da286d3c7518cf3eef9041229cf55d802da9dfca1a3ac56bf6ca98867f1a49","observation_id":"d87e016a-aa94-482e-9588-8e2b3779cc1f","resolution":{"observed_at":"2026-05-24T02:45:57.309779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Quantifying distributional model risk via optimal transport","venue":null,"work_id":"bc5307fc-0ea0-4576-841b-801f630b2bf0","year":2019},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:dd7aa63ac2b4099ca88a5b46ae428130d8ac424b67470d3365c7bd08e0b97983","observation_id":"dacb34b3-5f86-4b94-8c96-40188e426ed5","resolution":{"observed_at":"2026-05-24T02:45:57.305802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"On distributionally robust extreme value analysis","venue":null,"work_id":"13e71909-c073-4a12-8626-01e16300c995","year":2020},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:b41e42467e208a2ecda1848d2e2cc18c6ab61a587ade77ab61ce3f9f7137086e","observation_id":"7109f19d-647d-4219-86aa-28760e922f2d","resolution":{"observed_at":"2026-05-24T02:45:57.303011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/14-aap1011","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Arbitrage and duality in nondominated discrete-time models","venue":"The Annals of Applied Probability","work_id":"aba7feea-50b8-442b-871e-62f57f237aec","year":2015},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:65e7677f426dfb666ade02e203675162ab5626761baa1f09565a0bcee5dc4b8b","observation_id":"b33c313c-b3e8-4afe-857d-132803d6ce2c","resolution":{"observed_at":"2026-05-24T02:43:47.340094Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Langevin algorithms for very deep neural networks with application to image classification.Procedia Computer Science, 222:303–310","venue":null,"work_id":"cac1417e-c45d-4e3b-9f86-81d428cec948","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:72465124c0d988cdb4ff31be77c54b29bb5f2f52082e670d67838f68be360ffe","observation_id":"f3f0249c-0f69-4f02-a9b1-f7b0bbaa504e","resolution":{"observed_at":"2026-05-24T02:45:57.300195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Langevin algorithms for markovian neural networks and deep stochastic control","venue":null,"work_id":"4372348d-3e42-4615-8ece-a28fa01b4881","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:6027a3ada6ddad6360d30bf8c948d9f1290f2659388c248a3193e6453889834c","observation_id":"7d7a8908-72bd-4bd4-8d20-bb2dc556aa1c","resolution":{"observed_at":"2026-05-24T02:45:57.296833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"The promises and pitfalls of stochastic gradient Langevin dynamics","venue":null,"work_id":"fb024d4d-b807-470f-a454-4ad5757b0fef","year":2018},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:230e3a4879b32b8a00a19b0613b1686d25a1b86bb4f73465b88e5925605e0ec3","observation_id":"370650ab-2e52-477b-b027-c711fee59d8c","resolution":{"observed_at":"2026-05-24T02:45:57.293220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Viability and arbitrage under knightian uncertainty.Econometrica, 89(3):1207–1234","venue":null,"work_id":"52242609-cf31-458e-b208-0539514d6eeb","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:198d1d65ef703c76c6a35cbca80c1e49288417a099a25a6f22ca0d04e3f8f3c0","observation_id":"6455fd67-dec9-49ea-a4c7-3d93da73ac83","resolution":{"observed_at":"2026-05-24T02:45:57.289954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"The robust superreplication problem: a dynamic approach.SIAM Journal on Financial Mathematics, 10(4):907–941","venue":null,"work_id":"49e5721a-b28a-4aca-b9c7-d427e9726f85","year":2019},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:6b5d6472409cef603dc08f077c973f7398fa04915177c5c63df75d28ce27f12a","observation_id":"6b245499-4d62-4f2c-802e-3dd695876775","resolution":{"observed_at":"2026-05-24T02:45:57.283469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"On stochastic gradient langevin dynamics with dependent data streams: The fully nonconvex case.SIAM Journal on Mathematics of Data Science, 3(3):959–986","venue":null,"work_id":"1c16db5d-6281-4f30-9bed-cda535798c3d","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:ea6479337e0e13367e38ec4de63e114eb685e4fdf3120865420e94184bdd4aae","observation_id":"350dad2c-8d2f-45cf-bd9e-3b7111a1342f","resolution":{"observed_at":"2026-05-24T02:45:57.280267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"On the convergence of stochastic gradient mcmc algorithms with high-order integrators.Advances in neural information processing systems, 28","venue":null,"work_id":"9b0af4ad-633d-46de-be27-01ded210b789","year":2015},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:e944a4995a959cb1c7109700741502471febea7a35ca1dbb822d39ed6b09d369","observation_id":"c0aac11e-e08a-444d-b908-954de6df7cb2","resolution":{"observed_at":"2026-05-24T02:45:57.276996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Distributionally robust linear and discrete optimization with marginals.Operations Research, 70(3):1822–1834","venue":null,"work_id":"5ec04cc7-f27f-43c5-8be0-da76c932ae4b","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:dde50dadcd71916d720469ca2ecc0f1a303eaac6e51f7779271422e3a62cf132","observation_id":"fa592cb5-ba95-4c53-ae35-86916411b934","resolution":{"observed_at":"2026-05-24T02:45:57.270264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A robust learning approach for regression models based on distributionally robust optimization.Journal of Machine Learning Research, 19(13):1–48","venue":null,"work_id":"2ec81c42-5ab7-415f-8248-d67e8e44b116","year":2018},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:948f69f6169fcfc535549e4248ab92e95a9fac25addef95fae27b3c87623cdcc","observation_id":"0f8dc65d-cdfd-44da-a8b0-0c9e204e2c4a","resolution":{"observed_at":"2026-05-24T02:45:57.267171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"On stationary-point hitting time and ergodicity of stochastic gradient Langevin dynamics.Journal of Machine Learning Research","venue":null,"work_id":"bf27d612-0960-4131-bc87-529ca4c6cb08","year":2020},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:cf64421113bc191c9b3b29610968b3823ccb9bd3c2860e647617a42a7aab4642","observation_id":"24a6fc14-7e31-4d65-9a9a-aafb045939f7","resolution":{"observed_at":"2026-05-24T02:45:57.263785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Duality formulas for robust pricing and hedging in discrete time.SIAM Journal on Financial Mathematics, 8(1):738–765","venue":null,"work_id":"34b5b405-79d9-4da5-b8fd-3cf4a3d693e6","year":2017},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:ac54fb4ba029842a7e9bfa84cdd87b9550808b57e553d485cccc32e664f1f489","observation_id":"f6c33344-716b-4529-b2f0-8e9c428a3d5f","resolution":{"observed_at":"2026-05-24T02:45:57.260398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Martingale optimal transport duality.Mathematische Annalen, 379:1685–1712","venue":null,"work_id":"401dba02-3c93-44a2-af9f-2fc4fabb47e6","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:dc1d555707d424e83df98cb4f2183007bdf0277eef4e7adb16b06afc200111df","observation_id":"05686ffd-873e-4187-a662-b77fde6a83df","resolution":{"observed_at":"2026-05-24T02:45:57.255859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.12248","last_updated":"2023-02-10T16:24:04Z","snapshot_observed_at":"2026-07-06T12:11:38.494286Z","submitted_at":"2021-11-24T03:39:18Z","title":"Non-asymptotic estimation of risk measures using stochastic gradient Langevin dynamics","version":2},"cited_work":{"arxiv_id":"2111.12248","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.12248","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Non-asymptotic estimation of risk measures using stochastic gradient Langevin dynamics","venue":null,"work_id":"26183fc1-4fd0-4c1c-88ea-aa0c7191cbdf","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2111.12248","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:411dc26132719d62f449737992776811b41a37750297f9175e766037b0630cf5","observation_id":"93532e9e-9dd2-4f76-8d84-50a9bb20b33b","resolution":{"observed_at":"2026-05-24T02:43:47.617059Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent","venue":null,"work_id":"613ca146-6c60-4a2f-8d5e-f5a34755de07","year":2017},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:ba63811f0cfa859c9fa9304f86ce7c1bf9ff21dcb32318eebb2d92c875706861","observation_id":"056558cf-e4ba-4313-ad19-0a747643b7eb","resolution":{"observed_at":"2026-05-24T02:45:57.252568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"User-friendly guarantees for the Langevin monte carlo with inaccurate gradient.Stochastic Processes and their Applications, 129(12):5278–5311","venue":null,"work_id":"44612583-9057-4738-9ae4-d2c7e98f75f3","year":2019},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:6f91b2dc6216a2596eee4e47293c26e750aa0f67908c5b14ad39ec1b60e087d8","observation_id":"6ab9dff2-4789-468b-8b2e-f6158742c4be","resolution":{"observed_at":"2026-05-24T02:45:57.249288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Distributionally robust optimization under moment uncertainty with application to data-driven problems.Operations research, 58(3):595–612","venue":null,"work_id":"3dcab0c9-74b1-4ec5-9dd0-19b2a336246a","year":2010},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:8f2cdef50578b530d42f8a71a9c8db7bd747c42e813713c750b59786b906fff2","observation_id":"af139661-92b3-44a6-99fc-4ca27cf72ac6","resolution":{"observed_at":"2026-05-24T02:45:57.242523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"An adaptively weighted stochastic gradient mcmc algorithm for monte carlo simulation and global optimization.Statistics and Computing, 32(4):58","venue":null,"work_id":"56dd4ae6-bb72-4484-9022-65dccc344692","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:9065b60fbddb6e5b9a917117fa375f5a39355f06286a55125f23482181205a72","observation_id":"137f29b5-a58b-4a61-b720-d6f9a2009cc0","resolution":{"observed_at":"2026-05-24T02:45:57.238887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/105051606000000169","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A theoretical framework for the pricing of contingent claims in the presence of model uncertainty.The Annals of Applied Probability, 16(2):827 – 852","venue":"The Annals of Applied Probability","work_id":"bd88e911-019b-43c5-a75f-0ccb94966c10","year":2006},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:706283cc3e51524ca48176c536920719a3a76c5594b8a2e76b334a9170ead58b","observation_id":"f17c2b8b-705e-4a38-9986-87737d746406","resolution":{"observed_at":"2026-05-24T02:43:47.343832Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-11T22:20:37.244919+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T22:20:37.244919+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Martingale optimal transport and robust hedging in continuous time.Probability Theory and Related Fields, 160(1-2):391–427","venue":null,"work_id":"71cf9512-be38-495d-99b4-bdd840a54000","year":2014},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:260292fa26fc4d1e8c6d997d2a18ecbf0948be6ead4ca9e90febcf0bf05b7b37","observation_id":"c88809a3-f376-480c-85c9-02bd7ec078da","resolution":{"observed_at":"2026-05-24T02:45:57.227915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust hedging with proportional transaction costs.Finance and Stochastics, 18:327–347","venue":null,"work_id":"115c3fcc-2cef-47eb-a8ff-3e7cc79d75e8","year":2014},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:3c80852e33467e0d8844548a44d0780608802d909feb765647f0f01822e7d589","observation_id":"c855a05a-cd1b-457b-aeb6-76b603e3450d","resolution":{"observed_at":"2026-05-24T02:45:57.205998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Analysis of Langevin Monte Carlo via convex optimization.The Journal of Machine Learning Research, 20(1):2666–2711","venue":null,"work_id":"809eddbb-2a06-4fc2-81af-97b7746b6359","year":2019},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:8c0edc63dd04a41147d1b408097282c27afb3ed131dbc5c5679750ca9ed06d37","observation_id":"2b8660df-7f7b-4900-8f2c-da1113af8367","resolution":{"observed_at":"2026-05-24T02:45:57.202412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust risk aggregation with neural networks.Mathematical finance, 30(4):1229–1272","venue":null,"work_id":"62c4c8c2-cb0d-453b-bcab-f7c5446d0935","year":2020},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:41fc1ea1263addcc3d626e03616497057e89a6ffa5b78f0dea4b137be4af0812","observation_id":"6ca40da0-def0-4771-b56e-750c7d9c47bb","resolution":{"observed_at":"2026-05-24T02:45:57.198610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Risk, ambiguity, and the savage axioms.The quarterly journal of economics, 75 (4):643–669","venue":null,"work_id":"deee37b8-f7bc-4527-895a-291662d714fd","year":1961},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:023e23bdae9b54431ce0c760741bde86d461e4d81317edfc0b91e8a2b4d44e27","observation_id":"e016624f-706b-439a-9894-48638fe837a8","resolution":{"observed_at":"2026-05-24T02:45:57.191223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Intertemporal asset pricing under knightian uncertainty","venue":null,"work_id":"96fc7713-8add-4248-a1f5-4711938dd765","year":2004},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:0cedf0dd639d8d1e8cad6d49e90405c557389ce538a2b20c68f136196408b1b9","observation_id":"69b63754-fe4a-4257-a8ff-5258258fdd3b","resolution":{"observed_at":"2026-05-24T02:45:57.187412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Time-independent generalization bounds for sgld in non- convex settings.Advances in Neural Information Processing Systems, 34:19836–19846","venue":null,"work_id":"89f1efc5-221f-49ac-b91e-497a8e1f9893","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:462e118bbb94e9872414831ba7c01c17f9d4fe3e1ca2d1df5fd7c27a825b34ad","observation_id":"e9bf0882-056b-4959-b3ae-9f24884b2fcc","resolution":{"observed_at":"2026-05-24T02:45:57.183917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Portfolio optimization with ambiguous correlation and stochastic volatilities.SIAM Journal on Control and Optimization, 54(5):2309– 2338","venue":null,"work_id":"e7f5668e-d37c-46cc-bb1e-f6da63f241b0","year":2016},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:863ee0af68ade917c2f386beaa13b4155dc568140ecc9dee8a881d19cc81b79c","observation_id":"8b212eaf-8908-4519-97d6-b089f0f79627","resolution":{"observed_at":"2026-05-24T02:45:57.180132Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Variationally inferred sampling through a refined bound","venue":null,"work_id":"b04ef2fa-78d0-459a-a1e3-cda9bd3f288f","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:d253d3964a0ed656e2224e2e1037ba4fa30af2806457662e4a26ad07757bd3e6","observation_id":"e173afac-51d9-4911-aab9-317133f1fccc","resolution":{"observed_at":"2026-05-24T02:45:57.176184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Distributionally robust stochastic optimization with Wasserstein distance.Mathematics of Operations Research, 48(2):603–655","venue":null,"work_id":"79b1156a-3712-422c-bbdb-5b3f8dcfa5bb","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:fdc5559369a9a55a38d4335fea06b150359ae918815af97296ab8c0bede97d44","observation_id":"5a353c72-9344-493a-ba8e-61238bc44947","resolution":{"observed_at":"2026-05-24T02:45:57.172329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Wasserstein distributionally robust optimization and variation regularization.Oper","venue":null,"work_id":"818d9cd9-2d74-4809-8895-390581e0c280","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:c91d07f97d989c31a97714e0ad044777849b74c4a5999bcae199e75d49839cd0","observation_id":"43274e90-a98f-4ac2-84b8-93f5bb47b09f","resolution":{"observed_at":"2026-05-24T02:45:57.168932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Maxmin expected utility with non-unique prior.Journal of mathematical economics, 18(2):141–153","venue":null,"work_id":"444b84b5-8d29-4c89-8fe6-6adef420cf4c","year":1989},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:7293f674f2184e4d7cb643f33d2bc6557bfec4162b553578851316744ad29b85","observation_id":"1699f324-31e1-4c3a-bb16-293150f47112","resolution":{"observed_at":"2026-05-24T02:45:57.179909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A stochastic subgradient method for distributionally robust non-convex and non-smooth learning.Journal of Optimization Theory and Applications, 194(3):1014–1041","venue":null,"work_id":"468f09fc-34b5-4959-9715-d4006e0bbea3","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:c66bb959ce78a96b822b45b406324d7266c8e322cc4ecbe4a05719395b9374b9","observation_id":"fbef3da6-24d1-403d-a6da-6acbbff49478","resolution":{"observed_at":"2026-05-24T02:45:57.157772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust control and model uncertainty.American Economic Review, 91(2):60–66","venue":null,"work_id":"8be2e193-7339-4788-86b5-b7b5b5fd3e19","year":2001},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:b93ad21f1d70d02b57fefd297988ef3e4e8164a110b07255e65fbbf708ae4d6e","observation_id":"1b4ad295-876a-42f8-ac12-3dbd6512315b","resolution":{"observed_at":"2026-05-24T02:45:57.154036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Uncertainty quantification for plant disease detection using Bayesian deep learning.Applied Soft Computing, 96:106597","venue":null,"work_id":"0066ab98-971b-4bad-9ebc-ce597ffa29fa","year":2020},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:e7831604dc6d2fbfb76d901396b7f8f781106ca681f0c590ffde6e3e4fdec8dd","observation_id":"3894da6d-e0e0-46e5-85e0-b8d248f1068a","resolution":{"observed_at":"2026-05-24T02:45:57.146963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Model uncertainty, recalibration, and the emer- gence of delta–vega hedging.Finance Stoch., 21:873–930","venue":null,"work_id":"70396603-cf8f-4d82-87f3-4018612441a5","year":2017},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:bcfb887cc4eb3c752af274235759a2a11ae18229a29c9a7d928e156b93c69f1b","observation_id":"1504d14f-e0a7-46da-91a8-9edee486b7e7","resolution":{"observed_at":"2026-05-24T02:45:57.143277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Hedging with small uncertainty aversion.Finance Stoch., 21:1–64","venue":null,"work_id":"2edd1db5-ec71-43b5-a6f0-5c2005733099","year":2017},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:08e84e56a8157d58900173c7c87638039dd529cf08bd7547d2a90454003fafb4","observation_id":"dbc25685-81d2-4efb-ae05-d51ba700acb7","resolution":{"observed_at":"2026-05-24T02:45:57.139481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"71d643c0-e455-4745-a681-ee00f981862a","year":2020},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:4974e65a2fa01dd2a17b334a625f5ab08fc57f8b4b09556dedb91fa24bcc91d0","observation_id":"85d6e274-e547-437d-8f1f-bed5cf17509e","resolution":{"observed_at":"2026-05-24T02:45:57.131951Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust risk-aware reinforcement learning.SIAM Journal on Financial Mathematics, 13(1):213–226","venue":null,"work_id":"91214868-25f8-495f-8976-ca82195b5eec","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:0f89c0f58d76548852fae96e6da6113de818103224d36ddd8fc3d7cb163eb2b6","observation_id":"d63be46a-a706-4493-bc83-f165210837d6","resolution":{"observed_at":"2026-05-24T02:45:57.128044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Poisoning attacks on data- driven utility learning in games","venue":null,"work_id":"2b541624-f7ea-4be3-b62c-5e1d8639b9f8","year":2018},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:c6a175c23c41c500056d8e21aa886e9673001632c9c2d2f37c7104a2d0b9f519","observation_id":"43146af5-77c8-46e2-bb2e-1943b6a00d4b","resolution":{"observed_at":"2026-05-24T02:45:57.209713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Improvements on scalable stochastic Bayesian inference methods for multivariate hawkes process.Statistics and Computing, 34(2):85","venue":null,"work_id":"38ef1ee7-ac9e-4461-b1fc-f159293696fd","year":2024},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:100775c00377849f0b658194b03d4a918919350e4140a8b8e54679eba68b06c7","observation_id":"d49acf4d-fd73-437e-93c6-ccb89f841f98","resolution":{"observed_at":"2026-05-24T02:45:57.119943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.17109","last_updated":"2025-05-27T19:36:23Z","snapshot_observed_at":"2026-07-06T19:08:03.713404Z","submitted_at":"2024-08-30T08:50:39Z","title":"Sensitivity of causal distributionally robust optimization","version":2},"cited_work":{"arxiv_id":"2408.17109","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.17109","snapshot_observed_at":"2026-06-29T17:13:45.385914Z","title":"Sensitivity of causal distributionally robust optimization","venue":null,"work_id":"af2e6c70-5bda-41e9-9cb1-fe15800ceb55","year":2024},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2408.17109","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:2e92544787d8159c73d73b96811af785144ead31d2fe5583b8473df09f8cee27","observation_id":"882de3f1-a7a3-4911-b8f7-5dfbf3b2ba27","resolution":{"observed_at":"2026-05-24T02:43:47.740274Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust reinforcement learning via adversarial training with langevin dynamics.Advances in Neural Information Processing Systems, 33:8127–8138","venue":null,"work_id":"398075ee-f1ac-4a43-83ee-379fd9705ab5","year":2020},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:25d6537ff60b75d3492b328f2d1cab68e788272e32c7b33e02f05b086e79e022","observation_id":"fb6877f9-b9f8-47af-a364-093d939d6842","resolution":{"observed_at":"2026-05-24T02:45:57.116471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"84c770c4-d6be-4bc3-937a-08abc8f06950","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:29460cc293d01da82b8b4e8ff4509d6042e3c8519aab346c4cd76f93634c8728","observation_id":"d34b6287-eb2a-47e7-a3e2-9446bf0d4f46","resolution":{"observed_at":"2026-05-24T02:45:57.112612Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A smooth model of decision making under ambiguity.Econometrica, 73(6):1849–1892","venue":null,"work_id":"42bbf99b-b6d1-49cd-b6d2-732e3a4bbf1b","year":2005},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:d26ead67d42dfb082ce3b3ff60f26e4f931bcd5ffb6312b05ba288b76334dd55","observation_id":"5830f574-a23b-4656-bfba-b1363e7a8306","resolution":{"observed_at":"2026-05-24T02:45:57.226308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Houghton Mifflin","venue":null,"work_id":"7fb996cf-4a76-4962-be94-68ca9fab3d89","year":1921},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:4aa90dc319903507eb3b054f4df1e3a527cef156514b6ba8294dc86babc428c2","observation_id":"109bf726-24c1-49d5-a6b1-7fc6f1ac2373","resolution":{"observed_at":"2026-05-24T02:45:57.102472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Appointment scheduling under time-dependent patient no-show behavior.Management Science, 66(8):3480–3500","venue":null,"work_id":"8895db42-ea87-45ac-946e-aa9bc5886f4a","year":2020},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:e307721021427e3d9efd8f50ed35b7084a038a1e8003b48c29cab7be784c809a","observation_id":"831d1a5f-e167-4acd-b3c2-bab1eda63cde","resolution":{"observed_at":"2026-05-24T02:45:57.098539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.05973","last_updated":"2023-12-10T19:18:42Z","snapshot_observed_at":"2026-07-06T16:59:31.970458Z","submitted_at":"2023-12-10T19:18:42Z","title":"Risk measures based on weak optimal transport","version":1},"cited_work":{"arxiv_id":"2312.05973","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2312.05973","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Risk measures based on weak optimal transport","venue":null,"work_id":"4649195e-631c-4744-ae88-55c7b91a1b22","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2312.05973","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:1758433250b637add224ea466f0d67901b0f1493349fbd3b345f0976d6eaaf56","observation_id":"2c5250e7-35f5-4128-a0c6-7002f3fda138","resolution":{"observed_at":"2026-05-24T02:43:47.715692Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Principled learn- ing method for Wasserstein distributionally robust optimization with local perturbations","venue":null,"work_id":"6a41ea42-156a-4378-a014-645db71b2857","year":2020},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:278817d202cbe219c78c34e627ba6883f482fa3fd16ab1ea0dcfefc23622d1ea","observation_id":"c099bfcf-f0bd-4862-86a5-6eed64303d6c","resolution":{"observed_at":"2026-05-24T02:45:57.095139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"On the adversarial robustness of robust estimators.IEEE Transactions on Information Theory, 66(8):5097–5109","venue":null,"work_id":"e6b0cb04-8b00-4caa-af92-27cb4349b89f","year":2020},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:d42d6ee29d1f50127ca9d6f6aaf8d73582b87350c9c1ec91c39808f576da0e26","observation_id":"741c1dc5-4453-42bc-90f4-70c9035548b3","resolution":{"observed_at":"2026-05-24T02:45:57.240772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.14259","last_updated":"2026-05-20T07:10:30Z","snapshot_observed_at":"2026-07-06T14:35:49.168830Z","submitted_at":"2022-12-29T11:06:28Z","title":"Bipolar Theorems for Sets of Non-negative Random Variables","version":5},"cited_work":{"arxiv_id":"2212.14259","doi":null,"metadata_source":"pith","pith_arxiv_id":"2212.14259","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bipolar Theorems for Sets of Non-negative Random Variables","venue":"math.PR","work_id":"de1e566c-06b9-47b3-ad08-dd3adb8fcd4b","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2212.14259","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:de64f107f978528f6b7ac606acdf57680a84937df22ad05eec09b2c5a81d3523","observation_id":"1904573e-72ab-4f96-9852-91ff4c2a03ef","resolution":{"observed_at":"2026-05-24T02:43:47.710562Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.03822","last_updated":"2024-03-28T17:14:17Z","snapshot_observed_at":"2026-07-06T15:39:18.493629Z","submitted_at":"2023-06-06T16:09:16Z","title":"Swing contract pricing: with and without Neural Networks","version":4},"cited_work":{"arxiv_id":"2306.03822","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.03822","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Swing contract pricing: A parametric approach with adjoint automatic differentiation and neural networks","venue":null,"work_id":"1831c54c-80fb-4801-9ec0-23b9d7b2b897","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2306.03822","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:5d6081eb3d7a11cf706ff18ed55af720e6f8890dbe11aed350108167d90cf4c6","observation_id":"9ae63a6f-8990-4cf0-9da2-4bda7acba646","resolution":{"observed_at":"2026-05-24T02:43:47.705488Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Preconditioned stochastic gradient Langevin dynamics for deep neural networks","venue":null,"work_id":"36d9af47-53f5-4087-9e1b-b9f8912e8411","year":2016},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:3cd7a671aa7155c76262e8656a34bb57f17b4efc1fc0afc3daacb85a3876259e","observation_id":"9f66112f-3826-44e0-bc5e-7cbb8684ec89","resolution":{"observed_at":"2026-05-24T02:45:57.087951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"High-order stochastic gradient thermostats for Bayesian learning of deep models","venue":null,"work_id":"fa2455c9-a0fc-4c0e-a2f1-5d7abe4bcf20","year":2016},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:24f4136074113d70d6c173f5106728a91aa074521433d92d6177ecf585e2d828","observation_id":"981f2fbe-d8d0-4044-8ca7-5b89ecd8b7de","resolution":{"observed_at":"2026-05-24T02:45:57.084497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2305.19004","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T10:33:18.766907Z","title":"Policy gradient algorithms for robust mdps with non-rectangular uncertainty sets","venue":null,"work_id":"c83a9939-b3e3-4d6b-8e71-21b41149e0b9","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:740767449f45618db77527ec7b155e551bef18ccf74ce06c85aa787f1da4e3ab","observation_id":"b1fa5dd7-7848-4422-89bc-9bb15ddbe553","resolution":{"observed_at":"2026-05-24T02:43:47.700333Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Scalable mcmc for mixed membership stochastic blockmodels","venue":null,"work_id":"7f643db1-75c4-433e-97f3-f0bd3591cf6b","year":2016},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:12045a177ce5b4a466e86516e0d85d83c18e491e55fdff84985a6220809d2a10","observation_id":"6aeb351a-c0cd-41cf-9bd8-11ecb0d597c7","resolution":{"observed_at":"2026-05-24T02:45:57.081433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.13937","last_updated":"2024-03-02T12:55:56Z","snapshot_observed_at":"2026-07-06T11:13:44.890922Z","submitted_at":"2021-05-28T15:58:48Z","title":"Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks","version":3},"cited_work":{"arxiv_id":"2105.13937","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2105.13937","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks","venue":null,"work_id":"f46002a2-7aab-47ae-835c-dcbe58bce2d2","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2105.13937","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:ac5a1c7d0ef91448f4fe3467ed01b484d27556151bc9de5c27b68dc7ab2e25ea","observation_id":"26ba7677-1724-47c3-8015-8ce9437a82b3","resolution":{"observed_at":"2026-05-24T02:43:47.688619Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13193","last_updated":"2024-06-30T16:07:10Z","snapshot_observed_at":"2026-07-06T14:09:37.067918Z","submitted_at":"2022-10-24T13:10:06Z","title":"Langevin dynamics based algorithm e-TH$\\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient","version":3},"cited_work":{"arxiv_id":"2210.13193","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.13193","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Langevin dynamics based algorithm e-TH ε O POULA for stochastic optimization problems with discontinuous stochastic gradient","venue":null,"work_id":"862348db-c713-4ec0-a438-030ec1fc55b1","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2210.13193","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:e558b4bf60c70de3974da19c8684686dfd85316cebe14d304159f071db743c6c","observation_id":"3192b994-b8e4-4f10-b0da-24f3fa6af5d4","resolution":{"observed_at":"2026-05-24T02:43:47.639612Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Non-asymptotic estimates for tusla algorithm for non-convex learning with applications to neural networks with ReLU activation function.IMA Journal of Numerical Analysis","venue":null,"work_id":"1de290b0-010a-4c32-985b-b049b0b1b8c4","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:8cb8a629333f3cbe47e6e4d6e0c83fc798b9c6916bf1100de23dbc8c2d5ce533","observation_id":"6cb84953-e660-4a01-aea8-ea6c22a2939c","resolution":{"observed_at":"2026-05-24T02:45:57.078430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17602","last_updated":"2026-07-16T04:41:12Z","snapshot_observed_at":"2026-08-02T01:40:39.685098Z","submitted_at":"2025-02-24T19:36:47Z","title":"A stochastic smoothing framework for nonconvex-nonconcave minEmax problems with applications to Wasserstein distributionally robust optimization","version":2},"cited_work":{"arxiv_id":"2502.17602","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.17602","snapshot_observed_at":"2026-07-17T01:20:34.521445Z","title":"arXiv preprint arXiv:2502.17602 , year=","venue":null,"work_id":"231690fa-d621-4d3d-8fcf-e89ca9d20de9","year":2025},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2502.17602","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:75ceb5bb7391c67f612936e14fc07c99929bfd621986fe76d613bc07d06a567f","observation_id":"49efc588-6368-4840-af66-d3d160965aea","resolution":{"observed_at":"2026-07-17T01:20:34.521445Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Distributionally robust q-learning","venue":null,"work_id":"b6af9cd3-1e22-431b-bd9e-5581d08e04a0","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:705997eca796fc0eac036f3e0f861f100e3735187a5d0cd5f16682b0f85911c4","observation_id":"89890732-3452-4025-bdb2-4f13be0da70b","resolution":{"observed_at":"2026-05-24T02:45:57.106003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.00796","last_updated":"2020-02-18T10:14:31Z","snapshot_observed_at":"2026-07-30T20:44:03.197667Z","submitted_at":"2019-12-02T14:03:55Z","title":"Differential Bayesian Neural Nets","version":2},"cited_work":{"arxiv_id":"1912.00796","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1912.00796","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Differential bayesian neural nets","venue":null,"work_id":"e1d37832-a3c1-4222-a790-bee7a20f0839","year":1912},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/1912.00796","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:eeaae231917059789a0c24b292a854552014486dc7905b38343f44eb047a14c5","observation_id":"1bccca8d-1827-4a97-8487-c961a9575d85","resolution":{"observed_at":"2026-05-24T02:43:47.683463Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Taming neural networks with tusla: Nonconvex learning via adaptive stochastic gradient langevin algorithms.SIAM Journal on Mathematics of Data Science, 5(2):323–345","venue":null,"work_id":"956a0267-8b2f-464e-bd54-91c4c29d9805","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:8b2c5e8475e3096b5671e67c83a7537d6d879dbbfd22c2a2cab2f308b43295a2","observation_id":"cd220db1-f954-4329-96b1-25b2e9ff5dc2","resolution":{"observed_at":"2026-05-24T02:45:57.071835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A complete recipe for stochastic gradient mcmc.Advances in neural information processing systems, 28","venue":null,"work_id":"95da031a-333f-464d-89d9-35e02de94ae0","year":2015},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:92e246fded1885e8d444ad1d9e14156708bc23ae9794a44aa432e74aad64ae20","observation_id":"c325ec5b-2cc8-467e-991b-a877a71aaaa9","resolution":{"observed_at":"2026-05-24T02:45:57.068447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Ambiguity aversion, robustness, and the variational representation of preferences.Econometrica, 74(6):1447–1498","venue":null,"work_id":"ceb57ede-b2cc-419c-b2ac-5a10f847ab79","year":2006},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:76e167256a26d63d814f68330b9c865c3157a608c50f309515f663d31a42b56e","observation_id":"5717bac9-26a0-410f-840c-d589e8036d49","resolution":{"observed_at":"2026-05-24T02:45:57.065332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Appointment scheduling with limited distributional information.Management Science, 61(2):316–334","venue":null,"work_id":"5c153c95-eafd-4386-b3cc-702e9a3f285c","year":2015},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:a265c0aac57c12b3743c252b70a59a79be15daf6f0f9fd11dd48395a9f06c816","observation_id":"19de4941-25ca-4fc5-ba13-61fc2b518dc1","resolution":{"observed_at":"2026-05-24T02:45:57.060130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust utility maximization in nondominated models with 2BSDE: the uncertain volatility model.Mathematical Finance, 25(2):258–287","venue":null,"work_id":"272c1480-ad22-4c87-9205-be460c527641","year":2015},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:693aa4734988c44f2da23e3a4ad6bee9743c697079ae9933b235c2eae5815e24","observation_id":"d69b846b-26ed-489c-a246-fadf22f44053","resolution":{"observed_at":"2026-05-24T02:45:57.056666Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations.Mathematical Programming, 171(1-2):115–166","venue":null,"work_id":"cf1bf19c-494f-474b-8f89-bbe7b8d72441","year":2018},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:0771974a55873c21448a4ad954f7ad85558258237872a41c90c21125335b2cbe","observation_id":"042b17fb-b175-4ac5-80db-7036ce6a3c40","resolution":{"observed_at":"2026-05-24T02:45:57.053233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Latent dirichlet analysis of categorical survey responses.Journal of Business & Economic Statistics, 40(1):256–271","venue":null,"work_id":"b34d49aa-1c13-4881-90bd-62217992839f","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:c5adb8c39a1aa042c8ae65ff69857ba6b853fb3d7fc0e4a20aa2b20207937a90","observation_id":"a1d17774-1cc3-469e-812d-e2cf0b6e84fc","resolution":{"observed_at":"2026-05-24T02:45:57.049687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Stochastic gradient markov chain monte carlo.Journal of the American Statistical Association, 116(533):433–450","venue":null,"work_id":"45d70f9c-66a2-414e-a128-1773012ec774","year":2021},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:0866eda09161044d610d723ad218ba57e00c25eee398468ddfe016a65d258b91","observation_id":"41836818-2e90-4b42-96c8-3a0688d0f12f","resolution":{"observed_at":"2026-05-24T02:45:57.135808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.14340","last_updated":"2024-08-12T03:02:44Z","snapshot_observed_at":"2026-07-06T14:10:24.345006Z","submitted_at":"2022-10-25T21:07:14Z","title":"A parametric approach to the estimation of convex risk functionals based on Wasserstein distance","version":2},"cited_work":{"arxiv_id":"2210.14340","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.14340","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nendel and A","venue":null,"work_id":"b95e8671-6f30-4998-9aa1-2c9d3de5cfb1","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2210.14340","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:61aa3ba9c429ec8be22d7cd4e3aef314d56f90d3dc61592a99d250de8c275e61","observation_id":"1f394a1a-4ef3-4c75-87b3-785a1f3e2b63","resolution":{"observed_at":"2026-05-24T02:43:47.670443Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/ejp.v18-2358","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Superreplication under volatility uncertainty for measurable claims.Electronic Journal of Probability, 18(none):1 – 14","venue":"Electronic Journal of Probability","work_id":"5991e3eb-ce34-4a9f-88e7-5aca2fca81a2","year":2013},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:f38f525bcd3c01c39f78a95b68faa5db98531fe85774ce435fe8318062d690e9","observation_id":"766e0f22-bb44-453b-8ef7-9d0329b01a60","resolution":{"observed_at":"2026-05-24T02:43:47.333064Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust utility maximization with l ´evy processes.Mathematical Finance, 28(1):82–105","venue":null,"work_id":"110fc2b4-c5c7-4065-af9b-3995f06b37e5","year":2018},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:02f872437ab9f296ae7f94e38c445b86a46e60f4e30a3fa71e60bf52fc15d0d5","observation_id":"026e2e74-f77e-4c64-8d7e-6b4d07c91a9b","resolution":{"observed_at":"2026-05-24T02:45:57.046355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00898","last_updated":"2024-06-20T15:50:17Z","snapshot_observed_at":"2026-07-06T13:59:00.116062Z","submitted_at":"2022-09-30T10:01:04Z","title":"Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty","version":3},"cited_work":{"arxiv_id":"2210.00898","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.00898","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust Q-learning algorithm for Markov decision processes under Wasserstein uncertainty","venue":null,"work_id":"91b83be4-f676-4832-850b-ee591beb0536","year":2022},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"cited_paper":"/paper/2210.00898","citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:e4f904b17d7b1ec08b171a3a22312b18e3764c8f7fbf9fcbf185093ec1fc44f1","observation_id":"cfffa923-9269-4b89-a7d6-bfbffb4efde4","resolution":{"observed_at":"2026-05-24T02:43:47.656633Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"A deep learning approach to data-driven model-free pricing and to martingale optimal transport.IEEE Transactions on Information Theory, 69(5):3172–3189","venue":null,"work_id":"4ce665a9-fc14-489b-b982-18b31bba79e6","year":2023},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:fef5aa519f632cd123292ce5e8b4fe8eeb85b2913c7e5d0b877210f0511d3974","observation_id":"55a38ff2-fa33-4f08-a07c-1b9e5173862d","resolution":{"observed_at":"2026-05-24T02:45:57.043041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Robust utility maximization in discrete-time markets with friction","venue":null,"work_id":"e9ac163e-30bf-4993-8aeb-fc8c9563d162","year":1912},"citing_paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems","version":4},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-05-24T02:38:47.015471Z"},"links":{"citing_paper":"/paper/2403.09532"},"observation_digest":"sha256:c55a1c4c3e9ad9d930553e3dbc73c7d08644ec3eab73afe38f9dabd7940733ed","observation_id":"f78a3127-643e-4dbd-9e8c-8487eb5e81de","resolution":{"observed_at":"2026-05-24T02:45:57.039614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2403.09532","last_updated":"2026-05-07T09:57:24Z","latest_version":4,"primary_category":"math.OC","snapshot_observed_at":"2026-07-06T17:44:41.822709Z","submitted_at":"2024-03-14T16:21:32Z","title":"Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":2,"verified_exact":17,"verified_fuzzy":79},"total_outbound_references":145},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 100 of 145 outbound references and 1 inbound Pith citation observation for arXiv:2403.09532."}