{"as_of":"2026-08-17T07:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e6366e15259a1f8478b7ee64ab40ebd9c7ea6fc159bc7babe00253b18f75c4d2","coverage":[{"denominator":91,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":91,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:10:52.007215Z","state":"measured"},{"denominator":93,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":93,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:09:59.848604Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-14T10:39:29.994847Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.01602","snapshot_observed_at":"2026-08-14T14:09:59.848604Z","title":"Solving high- dimensional optimal stopping problems using deep learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.03833","last_updated":"2019-08-11T00:40:43Z","snapshot_observed_at":"2026-08-15T03:21:22.751656Z","submitted_at":"2019-08-11T00:40:43Z","title":"Space-time error estimates for deep neural network approximations for differential equations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T14:09:59.848604Z"},"links":{"cited_paper":"/paper/1908.01602","citing_paper":"/paper/1908.03833"},"observation_digest":"sha256:82d798ff653eb41b6527f9b31ca81e18996e573bdcc125bf844026e5a0da36f4","observation_id":"e6b167d0-910c-4a88-92a0-56f2c274d66a","resolution":{"observed_at":"2026-08-14T14:09:59.848604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"cited_work":{"arxiv_id":"1908.01602","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.01602","snapshot_observed_at":"2026-08-14T10:39:29.994847Z","title":"Solving high-dimensional optimal stopping problems using deep learning","venue":"cs.CE","work_id":"ee398637-d059-4fce-9040-b95a854e4125","year":2019},"citing_paper":{"arxiv_id":"1908.10828","last_updated":"2019-08-28T16:57:17Z","snapshot_observed_at":"2026-08-15T13:57:47.042911Z","submitted_at":"2019-08-28T16:57:17Z","title":"Deep neural network approximations for Monte Carlo algorithms","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T10:39:29.513142Z"},"links":{"cited_paper":"/paper/1908.01602","citing_paper":"/paper/1908.10828"},"observation_digest":"sha256:958ff58355306b6122222c3a7f82fa36f387d09dd3dafaaa4cb5040b68c707c3","observation_id":"ade606b0-3af4-4b27-a98f-6173c870dacc","resolution":{"observed_at":"2026-08-14T10:39:29.998521Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1908.01602/citation-record","integrity":"/paper/1908.01602/integrity","json":"/paper/1908.01602/citation-record.json","paper":"/paper/1908.01602"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:50.944718Z","title":"American options: a comparison of numerical methods","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:50.944718Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:198c9f62b395b0a29aabf2cc2b36fb02d98f2754ebc3f998d3e32c0029a1e51c","observation_id":"147e1ada-0c03-40f2-ad50-81b0128d955b","resolution":{"observed_at":"2026-08-14T15:10:50.944718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:50.959516Z","title":"A simple approach to the pricing of bermudan swaptions in the multi-factor libor market model","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:50.959516Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:8423e196fb93aadd8c1e373a1636a17ca3a58bc2b2ab1978af00b0683eb1bbe6","observation_id":"2434c1c5-0685-4dd5-93a0-a0b64ec2ccf1","resolution":{"observed_at":"2026-08-14T15:10:50.959516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:50.971515Z","title":"Primal-dual simulation algorithm for pricing multidimensional american options","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:50.971515Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:b8f1676018790c3471144caca96e97d03d94e96a076864936cc707eff4e83a21","observation_id":"182c3207-a327-4e9c-8cb4-d67ab0e3fa9f","resolution":{"observed_at":"2026-08-14T15:10:50.971515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:50.984817Z","title":"Error analysis of the optimal quantization algorithm for obstacle problems","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:50.984817Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:c9bd42648f1b97b036d2dbe4ba574480a7b290683837d276c7394d3a534185e4","observation_id":"381e419d-d910-4eb5-9f7f-f21bb65a3603","resolution":{"observed_at":"2026-08-14T15:10:50.984817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:50.997799Z","title":"Numerical valuation of high dimensional multivariate american securities","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:50.997799Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:f28db3e29cf0b701f55e56f5ad01fb2683cb7189a9ef3b5058a2f1d6b34512c3","observation_id":"2baccaf8-6061-454f-a5ee-687b974516cd","resolution":{"observed_at":"2026-08-14T15:10:50.997799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:51.010125Z","title":null,"venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.010125Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:45cc29cca153735ec1a7c9c718e3601a2dad3d7e0101a1524aa25df3ab357037","observation_id":"9afb2bb1-8498-4fb3-a5fd-abbf8a76ab38","resolution":{"observed_at":"2026-08-14T15:10:51.010125Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:51.022543Z","title":"Implied stopping rules for American basket options from Markovian projection","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.022543Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:c67ca139782592fe97a4a9b7cce4e29033620249060f01bf7f8a4e0822167f27","observation_id":"a835650f-3903-4de6-9428-f5baee508b6a","resolution":{"observed_at":"2026-08-14T15:10:51.022543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.07300","last_updated":"2019-08-10T16:17:30Z","snapshot_observed_at":"2026-08-16T03:50:14.203605Z","submitted_at":"2018-09-19T17:10:07Z","title":"Pricing American Options by Exercise Rate Optimization","version":2},"cited_work":{"arxiv_id":"1809.07300","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.07300","snapshot_observed_at":"2026-08-14T15:10:52.652899Z","title":"Pricing American Options by Exercise Rate Optimization","venue":"q-fin.CP","work_id":"17c3b311-2669-45bc-b1eb-bcb690f97c37","year":2018},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.037493Z"},"links":{"cited_paper":"/paper/1809.07300","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:cce2d5572793e6bea3b6a9443348e31acd93b8fc92ef4838f1e1bfb8ea3f27ea","observation_id":"ee1f7a08-217c-4921-b080-ae0b667fe11d","resolution":{"observed_at":"2026-08-14T15:10:52.661586Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.05394","last_updated":"2020-01-05T14:55:06Z","snapshot_observed_at":"2026-08-15T14:00:35.339418Z","submitted_at":"2018-04-15T17:57:07Z","title":"Deep optimal stopping","version":4},"cited_work":{"arxiv_id":"1804.05394","doi":null,"metadata_source":"pith","pith_arxiv_id":"1804.05394","snapshot_observed_at":"2026-08-14T15:10:52.612347Z","title":"Deep optimal stopping","venue":"math.NA","work_id":"0ebdb0f9-a2e7-4cfd-9a77-e16873a8ba4d","year":2018},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.047297Z"},"links":{"cited_paper":"/paper/1804.05394","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:4fdc09ba62cff06b69f93c6e7e8fa05918877810a36729e8884f0d8b4a66fceb","observation_id":"1b0bb818-4e76-49df-bd32-80080130149f","resolution":{"observed_at":"2026-08-14T15:10:52.620850Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:51.063363Z","title":"Dynamic programming","venue":null,"work_id":null,"year":1966},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.063363Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:525560d82e6b90e7e51208b4b6e48f93ef8f638ce8dd4a89b361a49b2b86b3f8","observation_id":"71ae0f4e-3538-407b-8ad0-91a73ab4971e","resolution":{"observed_at":"2026-08-14T15:10:51.063363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:51.076265Z","title":"On the rates of convergence of simulation-based optimization algorithms for optimal stopping problems","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.076265Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:ba688f7a7e1ced16b8a96cd9e3edbf9a3c77c52c6f0fc1c3a8d006ba9a19d216","observation_id":"1e4fee34-a5ef-464e-b690-d314992cd1c5","resolution":{"observed_at":"2026-08-14T15:10:51.076265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:51.085142Z","title":"Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.085142Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:c910e39be602998fd832d6fff11ea7a5034d5ce8f87edad74da9fd8a60ad89e4","observation_id":"dfdd09f4-f7e0-4657-ae9b-8da6a49a4d16","resolution":{"observed_at":"2026-08-14T15:10:51.085142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:51.093861Z","title":"Solving optimal stopping problems via empirical dual optimiza- tion","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.093861Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:9020105fd41b20f4597eda8a505b6afd70db5b3a3ad97bc3a51522980f7f0e7e","observation_id":"f1237e76-1760-4872-861f-e2749bd2ce85","resolution":{"observed_at":"2026-08-14T15:10:51.093861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:55.086781Z","title":"True upper bounds for Bermudan products via non-nested Monte Carlo","venue":null,"work_id":"c08784e7-37f4-4fe9-a68b-d24f176cbd8c","year":2009},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.106378Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:e118bd5fda1216f4b308ea598cec6c6e74a8fefeb086c41e7dc3785a70248d1b","observation_id":"861b9e81-a883-4a75-afcd-425a59a1ffbd","resolution":{"observed_at":"2026-08-14T15:10:55.100632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:55.062949Z","title":"Pricing Bermudan options via multilevel approximation methods","venue":null,"work_id":"2e286b7f-d2a1-44f4-b6cc-951e653dfd97","year":2015},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.118777Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:585986f856349ddf7dd57bb829a9c9caa873ec94ed3b948869f43e2bfc1e754f","observation_id":"e357d850-2beb-4e5e-8d37-ad2ea53da272","resolution":{"observed_at":"2026-08-14T15:10:55.070643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:55.026850Z","title":"Multilevel simu- lation based policy iteration for optimal stopping—convergence and complexity","venue":null,"work_id":"d3d245d6-0574-4851-b84d-99d0a3d314c6","year":2015},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.125239Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:2753a654b4b1aca66ce948d9642c53fde29795cbf4b37c936436eab5e8f997e3","observation_id":"a0963bdb-5472-492e-859d-2893fd2dbfc5","resolution":{"observed_at":"2026-08-14T15:10:55.041944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.993346Z","title":"Multilevel dual ap- proach for pricing American style derivatives","venue":null,"work_id":"e64d4bf6-86ac-4c39-b2bc-432f53ac1911","year":2013},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.145267Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:16b081a1f3946258062008ec9cd687437e4a7292afaa37ff20bf8c32b9e63e42","observation_id":"b67713c1-19ba-402a-8bd9-ced87247d82c","resolution":{"observed_at":"2026-08-14T15:10:54.999580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.952024Z","title":"Policy iteration for Ameri- can options: overview","venue":null,"work_id":"29789652-37dd-4023-a251-bfdc1e0ab889","year":2006},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.152578Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:6c1aed74a3e8677d9eacccf658dad62da36ca5f8099452de6cb84022fe01bda4","observation_id":"a3726953-492a-4168-922c-d03fbeed9faa","resolution":{"observed_at":"2026-08-14T15:10:54.969641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.919692Z","title":"Enhanced policy iteration for American options via scenario selection","venue":null,"work_id":"4ec183ad-1a31-495c-bfe6-d60c2215d061","year":2008},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.166185Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:8d2b6d180265a303850aad7d9f3ab3f2f3bfd6116e52ce380bc864d012aff4a2","observation_id":"a01378d1-eca9-4023-8c57-57fc5a668414","resolution":{"observed_at":"2026-08-14T15:10:54.927598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.883531Z","title":"A primal-dual algorithm for BSDES","venue":null,"work_id":"abee999f-abaf-42d2-a659-3360a5bf61de","year":2017},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.181155Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:5a3acb7b39e87ae2e4ee217c8372bbf150dadbf15d5a295e52134445883c93eb","observation_id":"69cb21f9-ca86-40bd-9ac8-4623a8b79b98","resolution":{"observed_at":"2026-08-14T15:10:54.891663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.844414Z","title":"J., and Schumacher, J","venue":null,"work_id":"1e75cd23-0532-4d7d-8554-385ec2c4ce81","year":2008},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.195129Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:8f8eb0680fefcfacf89a411f249bb24f7f11d8d6d70787ee9ac738130f800d00","observation_id":"c2066b53-e5c0-40c6-af60-7e722b617d16","resolution":{"observed_at":"2026-08-14T15:10:54.853324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.817393Z","title":"Discrete-time approximation and Monte-Carlo simulation of backward stochastic differential equations","venue":null,"work_id":"3285684a-1b4c-42dd-b0b8-eb71a9f87ddf","year":2004},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.203671Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:72e95ce193dc8b36d5ac950f3212aaeadbd6a4063c85e16d2f2b2128399a70f8","observation_id":"177f2b31-9fb5-4b03-9618-94faf78bfa4f","resolution":{"observed_at":"2026-08-14T15:10:54.827089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.778434Z","title":"Improved lower and upper bound algorithms for pricing American options by simulation","venue":null,"work_id":"0e720b90-0cd1-485d-b360-65fb4dbce10e","year":2008},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.216144Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:cd3fb6d2ae4822989bac3a11ffa9c8bd6bbb939c66f101997e1324b43c2cd1f5","observation_id":"f3dfc4e8-c00c-44b3-9d76-50df4be8c0d4","resolution":{"observed_at":"2026-08-14T15:10:54.790256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.721115Z","title":"Pricing American-style securities using sim- ulation","venue":null,"work_id":"b320656b-82c3-4b58-b0b7-16d15f8f9ec9","year":1997},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.225728Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:a01a2b3250cef110323e83694137a3bdab4e797685c81e77a830f87c4cc5972d","observation_id":"ad9407c9-c550-49ef-b18a-92a0343141c3","resolution":{"observed_at":"2026-08-14T15:10:54.738686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.696836Z","title":"A stochastic mesh method for pricing high- dimensional American options","venue":null,"work_id":"ec24aa5e-edaa-4e8f-97c9-56ccf45e4df7","year":2004},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.234957Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:5482faf25ab68e862b97c22ab0af1977c0c8c91450202fe448250b7a53c5f972","observation_id":"e90c67f2-9af2-4611-aedf-02e615141ae7","resolution":{"observed_at":"2026-08-14T15:10:54.703572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.669685Z","title":"Pricing American options by sim- ulation using a stochastic mesh with optimized weights","venue":null,"work_id":"7e5c160f-9e81-4a53-b104-7385906a6b27","year":2000},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.249790Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:bd5a8e8f2da1cff079dd329b89393505ddab4bdb71dae7185fefccd966504272","observation_id":"8caac4cf-3bd7-4cc5-b3e8-0ea43db87088","resolution":{"observed_at":"2026-08-14T15:10:54.676504Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.639729Z","title":null,"venue":null,"work_id":"95009a6f-6045-42c3-a6fc-d609c4671296","year":1996},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.263780Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:bc03e893142aacdf61652c091219a6994a521e7b9e3c2f050ae2a5438288392d","observation_id":"b9443d39-e193-4304-a83a-8b252b3ddd18","resolution":{"observed_at":"2026-08-14T15:10:54.647238Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.610048Z","title":"Additive and multiplicative duals for American option pricing","venue":null,"work_id":"efa5f69b-bd51-4115-8b3b-94cd568ae8db","year":2007},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.273691Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:0cc24b8f9f8b8c013126a2ed923042465ad63f4f260e8b6ede0019d8cd2fafc4","observation_id":"ef6b2b72-2e8b-4d75-8a49-c0b6ce49b019","resolution":{"observed_at":"2026-08-14T15:10:54.617174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.576589Z","title":"A method for pricing American options using semi-infinite linear programming","venue":null,"work_id":"83d8ac18-3d31-40b0-9a16-d95a09636096","year":2014},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.281940Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:6defc254ce3a7d5f9f7be5742c39407f35cd91b4a42015ba2c63591703c6144d","observation_id":"80ce25be-46ec-4e6b-9fe2-fffaae046726","resolution":{"observed_at":"2026-08-14T15:10:54.586048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.08545","last_updated":"2017-01-30T11:16:48Z","snapshot_observed_at":"2026-08-14T21:18:57.149474Z","submitted_at":"2017-01-30T11:16:48Z","title":"Computing stable numerical solutions for multidimensional American option pricing problems: a semi-discretization approach","version":1},"cited_work":{"arxiv_id":"1701.08545","doi":null,"metadata_source":"pith","pith_arxiv_id":"1701.08545","snapshot_observed_at":"2026-08-14T15:10:52.559146Z","title":"Computing stable numerical solutions for multidimensional American option pricing problems: a semi-discretization approach","venue":"q-fin.PR","work_id":"17490bba-a89e-4de3-9d8c-327b12594302","year":2017},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.290777Z"},"links":{"cited_paper":"/paper/1701.08545","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:3f54930d6c3d6b614619605d73687853ecbbc4ca98094470be34d31786d36233","observation_id":"73d3389e-e0b1-40e3-9a3b-ee540ae7f43d","resolution":{"observed_at":"2026-08-14T15:10:52.569739Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:51.298262Z","title":"Approximation by superpositions of a sigmoidal function","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.298262Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:51eab5b803e70b9ca5ba6167b9b90a69e8595b6822e4f1dc8d1bedebcc908554","observation_id":"33610023-ef36-4636-8a96-f93ca0095e58","resolution":{"observed_at":"2026-08-14T15:10:51.298262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:51.310229Z","title":"Stochastic equations in infinite dimensions , vol","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.310229Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:df7413c2138363a404b87f36623b631f54a5d6e32cb1ec60a681277daf5b9ac5","observation_id":"3be43d40-e083-44cd-853d-8845645a9865","resolution":{"observed_at":"2026-08-14T15:10:51.310229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.503413Z","title":null,"venue":null,"work_id":"bcf875f6-ea2a-46c4-9bb7-7601531f38a7","year":1994},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.326089Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:81b6fe4c4981a00ce8d9669ab337890ca4357582db4939edb7c468a73a885c57","observation_id":"7ddda565-c775-447c-94e9-c1a7b35ddac6","resolution":{"observed_at":"2026-08-14T15:10:54.511881Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.463867Z","title":"V., Farias, V","venue":null,"work_id":"f2d039b4-00c6-4d32-8cc2-5eaf56b1261b","year":2012},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.334741Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:a6eeca797bc4c1b70c5b80c8dda3af095a5912a5d241456be62ce5b4e38c26ae","observation_id":"9dafb0f5-f8c3-43f5-a698-821dd6716c91","resolution":{"observed_at":"2026-08-14T15:10:54.477156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.427882Z","title":"Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic dif- ferential equations","venue":null,"work_id":"943deefd-668c-4e33-89f4-7ff256d3e626","year":2017},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.344530Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:159b255362916bfb092cf091c9bdfa4dc619c44d75a80ae0b12588f3fc780913","observation_id":"7eae67fd-a665-46b3-aae5-3adc36a04c5d","resolution":{"observed_at":"2026-08-14T15:10:54.436386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.381649Z","title":"Monte Carlo algorithms for optimal stopping and statistical learning","venue":null,"work_id":"eb0704a4-fef9-4218-acef-2b52c8bf3697","year":2005},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.352250Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:824b28054f1260de2b7ce0570f65feb4a048da19ade8122e4c8504834c12b31e","observation_id":"f1d764a1-e2fa-40a7-a23f-5f9bdaecb1d5","resolution":{"observed_at":"2026-08-14T15:10:54.404592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.340624Z","title":"A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options","venue":null,"work_id":"821f1c9f-7bf5-4a09-b536-f40efe3d4794","year":2007},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.363017Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:f7f6658e592e12d01db3b4d4ab7b7d8ad5f80bf688bf2e1e5a9d28aa807b6227","observation_id":"1e9194be-648d-4177-ba74-f1fc6c081913","resolution":{"observed_at":"2026-08-14T15:10:54.352262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.283717Z","title":null,"venue":null,"work_id":"8d02ac57-47f1-40eb-9e2d-81b207966fab","year":2019},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.377898Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:0d4fcbbd570fd549d5e8cf428344e254eae2f7b8c0b9211434785e1af1e2e3e9","observation_id":"61084653-36b8-4456-a148-37d281915874","resolution":{"observed_at":"2026-08-14T15:10:54.306303Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.233750Z","title":null,"venue":null,"work_id":"06ba3f1a-1323-4ae3-8e88-b8d2b2a84d07","year":2005},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.388693Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:53f0190c042efea16486a2207feb57d663586806ff62dc97984a7c884078bcf2","observation_id":"860664ff-a301-498c-b33f-dd429b82308c","resolution":{"observed_at":"2026-08-14T15:10:54.247444Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.190458Z","title":"Asymptotic Expansion as Prior Knowledge in Deep Learning Method for High dimensional BSDEs","venue":null,"work_id":"c51110c4-f869-467e-bcc2-bd04ae9f0032","year":2019},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.405506Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:0a6d5f220997421042073b4dc7f5785e462538e4b529757639d0e376d2b45766","observation_id":"6f00f889-e30d-4585-985e-d2995b8cfe26","resolution":{"observed_at":"2026-08-14T15:10:54.201629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.143852Z","title":"Convergence and biases of Monte Carlo estimates of American option prices using a parametric exercise rule","venue":null,"work_id":"f82e924b-8b53-4573-bf12-2799b03f7f77","year":2003},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.421318Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:3af5e2ef204ab14b62dfac70a3b33fc4b554168ec934d5dbc7cda1d0433a7c7a","observation_id":"2827d184-0bfb-4cad-bf74-05258f19ff12","resolution":{"observed_at":"2026-08-14T15:10:54.153440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.104071Z","title":"Monte Carlo methods in financial engineering , vol","venue":null,"work_id":"046b21b8-c85b-4e30-807b-3e1b4d97fa9f","year":2004},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.433040Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:2b7fad5593684596edda895ea9244f7ee9aa7f9fb83ac7e1aa0cd9adfe1ad295","observation_id":"c20dea14-96c4-4866-934c-bb0c293c3c2c","resolution":{"observed_at":"2026-08-14T15:10:54.116047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.065989Z","title":"Understanding the difficulty of training deep feed- forward neural networks","venue":null,"work_id":"f3ff6a37-104c-424a-a5af-299e91a2eb9f","year":2010},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.451876Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:d6873c8a5330618d721094025e9961790037244542286b8c61bdfb9565a2c1cb","observation_id":"e9fdd3bd-3bcf-47ba-ba06-fd0f2d336280","resolution":{"observed_at":"2026-08-14T15:10:54.080931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:54.028295Z","title":"A regression-based Monte Carlo method to solve backward stochastic differential equations","venue":null,"work_id":"5f961e7f-359b-4bf6-b380-4c7ff3e0fd40","year":2005},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.465583Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:48c885eac99e18c5cb9735e0a8e65b49d3f3c49072cab4be16420815783070cf","observation_id":"2f2769b5-0565-4a99-9bf9-b30656537431","resolution":{"observed_at":"2026-08-14T15:10:54.036560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.02227","last_updated":"2024-05-14T23:24:31Z","snapshot_observed_at":"2026-08-16T04:12:59.298237Z","submitted_at":"2018-07-06T02:53:45Z","title":"Polynomial time algorithm for optimal stopping with fixed accuracy","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.02227","snapshot_observed_at":"2026-08-14T15:10:51.475372Z","title":"A., and Chen, Y","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.475372Z"},"links":{"cited_paper":"/paper/1807.02227","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:db47d99e33cb25bfc69824905e711f8cfdbb3ce5c7850cb915ca507ac7e43984","observation_id":"e585cd05-3961-4f15-b68b-ed8a10ef300b","resolution":{"observed_at":"2026-08-14T15:10:51.475372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.11275","last_updated":"2019-12-02T21:51:11Z","snapshot_observed_at":"2026-08-15T22:15:47.666111Z","submitted_at":"2019-03-27T07:33:35Z","title":"Variance Reduction Applied to Machine Learning for Pricing Bermudan/American Options in High Dimension","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.11275","snapshot_observed_at":"2026-08-14T15:10:51.487319Z","title":"Machine Learning for Pricing American Options in High Dimension","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.487319Z"},"links":{"cited_paper":"/paper/1903.11275","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:963a8bc1ef64e3cd1775339f55bf22e09d29979f87b33a0aef7f01a1698cd67f","observation_id":"3701a09a-fd3d-4bf3-8149-075ac5cce6e0","resolution":{"observed_at":"2026-08-14T15:10:51.487319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.987764Z","title":"Nonlinear option pricing","venue":null,"work_id":"8956de62-cb9f-41f5-a636-253fae5372da","year":2014},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.494827Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:b3104e725325e74d530dd7143708298949ea34bfa105843772d802170ff9c3f4","observation_id":"8237a3c4-234c-4c96-bcdb-a0417a8ef435","resolution":{"observed_at":"2026-08-14T15:10:53.996679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.951043Z","title":"Solving high-dimensional partial differential equations using deep learning","venue":null,"work_id":"b161aa43-93e1-48da-87ae-975fdfcbbcc5","year":2018},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.508246Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:d6669d090b02774453831a7cdff2753c9b72b9c04182f706b005d6138f0b2020","observation_id":"011dc44a-47de-49c8-bb93-3aacc8bd63d3","resolution":{"observed_at":"2026-08-14T15:10:53.958218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.908854Z","title":"B., and Kogan, L","venue":null,"work_id":"d3eacf36-3b93-462c-b061-1f9592ed1511","year":2004},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.513833Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:cad36df5270cce48bc48ca3a282228a21189daebe6180578074f58fe8da7cb9c","observation_id":"fb9e8ca4-a2e4-4b8e-9e3f-f5ff5d121dd7","resolution":{"observed_at":"2026-08-14T15:10:53.919755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.870417Z","title":"Neural Networks 2 , 5 (1989), 359 – 366","venue":null,"work_id":"ddb0c52f-6ea9-4156-a921-03a6fa76d118","year":1989},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.522821Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:963cb8175bc4a184028fcfa837a68f217b5f5fa1891740f527f64607944fcb62","observation_id":"20196392-12f6-4e2a-bc08-d66bf0fcb362","resolution":{"observed_at":"2026-08-14T15:10:53.883528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.828158Z","title":"Batch normalization: accelerating deep network train- ing by reducing internal covariate shift","venue":null,"work_id":"d37e2f96-c8ab-44c9-8e55-bac83ba7ed31","year":2015},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.540795Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:9f015ff017a2804bc8b6f518a2d0005d0d24fb4d29e1f7d3cfb0d655a5cf767c","observation_id":"bcf55e27-488d-473f-8537-85ec8efb0498","resolution":{"observed_at":"2026-08-14T15:10:53.845999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.792876Z","title":null,"venue":null,"work_id":"ae5d320f-d4dc-4f43-9e07-5daada913925","year":2012},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.553160Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:ad4da375aeb489874774af20378d9f1590067c860a48194c5e91ff55934d3f07","observation_id":"f004ad7a-8950-4b2c-8eee-7f51146265b8","resolution":{"observed_at":"2026-08-14T15:10:53.800892Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.759323Z","title":"The duality of optimal exercise and domineering claims: a Doob- Meyer decomposition approach to the Snell envelope","venue":null,"work_id":"c13540a7-6d5b-4c4e-9219-7d604517be0d","year":2007},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.561984Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:660105e786c4b5547ca201b92dec253edf594ca2b90ebdee4d33dafdd61a4bbc","observation_id":"1f2ef7c6-aa26-42b4-823e-382ea3b9217a","resolution":{"observed_at":"2026-08-14T15:10:53.767462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.07123","last_updated":"2017-10-19T12:54:47Z","snapshot_observed_at":"2026-08-14T20:22:13.671868Z","submitted_at":"2017-10-19T12:54:47Z","title":"Strong convergence for explicit space-time discrete numerical approximation methods for stochastic Burgers equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.07123","snapshot_observed_at":"2026-08-14T15:10:51.571478Z","title":"Strong convergence for explicit space-time discrete numerical approximation methods for stochastic Burgers equa- tions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.571478Z"},"links":{"cited_paper":"/paper/1710.07123","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:e9c9db7e0bbfab8e622728f8ea14276dedaae5d5403b6b9ac28d0e37c91de4f3","observation_id":"4c46d2ec-af1d-404d-820a-17625ea11a51","resolution":{"observed_at":"2026-08-14T15:10:51.571478Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.736803Z","title":"R., and Powell, W","venue":null,"work_id":"a186765c-1afb-4b88-b060-eb26ce5661c9","year":2015},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.582047Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:9e84f0643bc4c1fab3e44e3e2b32367c47ff54bff91a58cd911e98abae41d257","observation_id":"4d65f93d-e49b-47ef-8d56-8e5ff26ff216","resolution":{"observed_at":"2026-08-14T15:10:53.743045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.716359Z","title":"Option Pricing","venue":null,"work_id":"2e388935-87a8-490a-8596-17b871d1f57d","year":2009},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.592977Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:76f34c21350035b0251a2792c1d754521eb4bb094d5ed6049b750872c147f145","observation_id":"deb408ed-0507-45dd-b7d7-a94e56661947","resolution":{"observed_at":"2026-08-14T15:10:53.722906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:51.601761Z","title":null,"venue":null,"work_id":null,"year":1991},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.601761Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:994c3c285069a0064149fbb96772b44a60b98f517ea4835a6f20276d9a6c15ae","observation_id":"f5719683-60dd-40db-a3fd-f75f573af3b8","resolution":{"observed_at":"2026-08-14T15:10:51.601761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.662310Z","title":"Adam: a method for stochastic optimization","venue":null,"work_id":"da8f8e56-d8b5-4085-a184-eff7fc0386fd","year":2015},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.611153Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:d9d031c771d215b08d4136a4aabd983124bbd3464e6a51feca6a4c2847e83fa7","observation_id":"e03f42ac-9683-492c-9a5a-fcf8f34b591b","resolution":{"observed_at":"2026-08-14T15:10:53.668627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.637721Z","title":"Probability theory","venue":null,"work_id":"a9ce60e1-664b-4ced-b8ba-f133c2924f00","year":null},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.619065Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:e88bd84a0a5b19b4411c0e4562f9847687d7314b6cc017483027a88c515f52c7","observation_id":"3b5f8d6e-ead9-490a-a0ea-9755f7c2f049","resolution":{"observed_at":"2026-08-14T15:10:53.644952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.571278Z","title":"E., and Platen, E","venue":null,"work_id":"0d910434-416d-4ed0-9dde-2dbdabe1bbe2","year":1992},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.647852Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:78e433c1e774ba10eff7bba36b90738b29e2f3f8753c3225d28418981e76c9a8","observation_id":"5c491ee8-571d-493a-9650-060b00abaaeb","resolution":{"observed_at":"2026-08-14T15:10:53.580408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.545165Z","title":"A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time","venue":null,"work_id":"4dbf6643-cf24-47d5-ad93-c6c0e5316ceb","year":2008},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.667386Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:bd19ee448a7f664423d128b7f88786a1adebe03c5a03ed155c2b1087db9112c2","observation_id":"19e77252-db81-47dc-8a33-75e49a518fc4","resolution":{"observed_at":"2026-08-14T15:10:53.553638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.501404Z","title":"A review on regression-based Monte Carlo methods for pricing Amer- ican options","venue":null,"work_id":"086f2925-4b5a-4a18-a564-23e2c2ecd166","year":2010},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.681217Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:9258c32dc4d004f8efd654360d56ba7f0b6bf7d2d74b7b9e01adbd4f7bd6a56b","observation_id":"0f182dc2-8d97-4abb-b312-8ef4dd6d5a0c","resolution":{"observed_at":"2026-08-14T15:10:53.508329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.466765Z","title":"Pricing of American options in discrete time using least squares estimates with complexity penalties","venue":null,"work_id":"b3ae37cd-b153-4c7d-a39d-1454fb564eb3","year":2012},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.688396Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:cbfb7de20f20df3071afcd2f139e390d3f857ba70a1c974f062c93c673ab5163","observation_id":"ce3a4762-e999-4d5c-98db-7d77d6b4fbfe","resolution":{"observed_at":"2026-08-14T15:10:53.477564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.434247Z","title":"Pricing of high-dimensional American options by neural networks","venue":null,"work_id":"226c04b9-fe15-452c-9b2d-0b99e513ca28","year":2010},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.699311Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:63f66e6ceba8a538a96bbadfdd0b88a3991019bbf6d501e55e754bf16276935e","observation_id":"6179c20b-2f76-41f3-95fe-b5f9126579da","resolution":{"observed_at":"2026-08-14T15:10:53.443243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.388338Z","title":"Upper bounds for Bermudan options on Markovian data using nonparametric regression and a reduced number of nested Monte Carlo steps","venue":null,"work_id":"8bd5a037-7037-45c9-b38c-8a736a1851c1","year":2008},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.711256Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:0227bb444705ed1687b679c087131747baf7538ecc596eded264ad960277ea4c","observation_id":"4057f991-a731-43a6-80e1-d3afdc007bff","resolution":{"observed_at":"2026-08-14T15:10:53.407289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.357225Z","title":"Iterative construction of the optimal Bermudan stopping time","venue":null,"work_id":"23351f2f-39a7-4fcb-99dc-e4542906d914","year":2006},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.731318Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:3fd97cfe2c763809b9dda1821f171c40144b94f44211376fcd1e30805dbeccbe","observation_id":"68c39673-a250-4b5a-b031-37a4d01e327e","resolution":{"observed_at":"2026-08-14T15:10:53.367035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.321702Z","title":"V., and Gusyatnikov, P","venue":null,"work_id":"d60e73df-f4a5-4032-82c6-d63fe2a2ee7c","year":2014},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.743704Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:b178007430e0eac70d2c77387a0f3349cd9248223e35e1ddf5c17b82635d26b1","observation_id":"efce755b-3b7e-4c11-8974-2a29bfaf0f3c","resolution":{"observed_at":"2026-08-14T15:10:53.334195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1102.4666","last_updated":"2011-02-23T06:17:16Z","snapshot_observed_at":"2026-08-15T04:53:57.237302Z","submitted_at":"2011-02-23T06:17:16Z","title":"A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options","version":1},"cited_work":{"arxiv_id":"1102.4666","doi":null,"metadata_source":"pith","pith_arxiv_id":"1102.4666","snapshot_observed_at":"2026-08-14T15:10:52.384653Z","title":"A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options","venue":"math.PR","work_id":"049124ee-85b6-42ce-b4a7-804946e77191","year":2011},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.756408Z"},"links":{"cited_paper":"/paper/1102.4666","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:4207e39d8d06e2efd702ee59e1cced12069253349d7f2b2e9d156533e8ff76fc","observation_id":"975f6a63-fa2f-4035-a348-7ef65649e6f0","resolution":{"observed_at":"2026-08-14T15:10:52.403209Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.293232Z","title":"Introduction to stochastic calculus applied to finance, second ed","venue":null,"work_id":"0d27e877-f310-4a43-9a40-407b69264d84","year":2008},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.766290Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:176fb054a24b2e79d9e6ebb456f24c4f9e2d4b0116cc756ba920c424cba16b39","observation_id":"225148a2-9b6b-401b-885b-5839b0bbaaec","resolution":{"observed_at":"2026-08-14T15:10:53.305702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.06474","last_updated":"2020-12-02T13:37:09Z","snapshot_observed_at":"2026-08-14T16:05:51.300119Z","submitted_at":"2019-07-15T12:47:55Z","title":"Neural network regression for Bermudan option pricing","version":3},"cited_work":{"arxiv_id":"1907.06474","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.06474","snapshot_observed_at":"2026-08-14T15:10:52.321093Z","title":"Neural network regression for Bermudan option pricing","venue":"math.PR","work_id":"e58ea5ae-31d4-48ca-8ed8-6835049038b1","year":2019},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.778202Z"},"links":{"cited_paper":"/paper/1907.06474","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:f97fb836d167820c88dfd80cb68b5251dff1f4fd9ac86420e1eaae4329eaf54f","observation_id":"c7c39200-0c14-43ae-9482-ff81c36a5d98","resolution":{"observed_at":"2026-08-14T15:10:52.328291Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1604.03317","last_updated":"2016-04-12T09:26:35Z","snapshot_observed_at":"2026-08-14T22:02:04.834578Z","submitted_at":"2016-04-12T09:26:35Z","title":"Pricing American options using martingale bases","version":1},"cited_work":{"arxiv_id":"1604.03317","doi":null,"metadata_source":"pith","pith_arxiv_id":"1604.03317","snapshot_observed_at":"2026-08-14T15:10:52.279214Z","title":"Pricing American options using martingale bases","venue":"math.PR","work_id":"4ef72bbf-f158-4293-80ba-8f793d962ff9","year":2016},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.789019Z"},"links":{"cited_paper":"/paper/1604.03317","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:1f966bd4591ac0114f9738734151c0486d28d7d41aa0b663d6113950a0770b86","observation_id":"e6dfe8fe-e22b-4b56-9a1a-1598269fe3bb","resolution":{"observed_at":"2026-08-14T15:10:52.290930Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.05672","last_updated":"2020-07-24T08:25:36Z","snapshot_observed_at":"2026-08-14T17:29:47.995898Z","submitted_at":"2019-01-17T08:02:22Z","title":"Pricing path-dependent Bermudan options using Wiener chaos expansion: an embarrassingly parallel approach","version":2},"cited_work":{"arxiv_id":"1901.05672","doi":null,"metadata_source":"pith","pith_arxiv_id":"1901.05672","snapshot_observed_at":"2026-08-14T15:10:52.221173Z","title":"Pricing path-dependent Bermudan options using Wiener chaos expansion: an embarrassingly parallel approach","venue":"q-fin.CP","work_id":"2f88ff93-4cbb-4ec2-81e0-4454ccccaf94","year":2019},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.804943Z"},"links":{"cited_paper":"/paper/1901.05672","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:025323121af9bf77c23649a36b653624519ecd147a0c9a8144ba5b11c377eb13","observation_id":"4df29bdb-cb1a-4c0c-9972-aac2c559b51f","resolution":{"observed_at":"2026-08-14T15:10:52.232595Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.243246Z","title":"A., and Schwartz, E","venue":null,"work_id":"1ae38531-2b1f-48ab-a754-2af527a152e7","year":2001},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.816593Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:5ea9b76908dc9b9bc1fdf9d29879a51eff8cac456f64e49c0fa42fdbde7041a6","observation_id":"7581aead-8578-47af-add8-e83ae44c744b","resolution":{"observed_at":"2026-08-14T15:10:53.251761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.195294Z","title":"W.A fast and accurate FFT-based method for pricing early-exercise options under L´ evy processes.SIAM J","venue":null,"work_id":"db2252e6-cfa7-4363-8b9a-3c5f9d130512","year":2008},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.825296Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:d9bd394f47ced97f2709a9dd81a52895a0faddd2553ff3bed56ccf1ec51eb190","observation_id":"c0e5b9d0-4fcc-4d29-97d5-ec001462cad1","resolution":{"observed_at":"2026-08-14T15:10:53.203890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.149958Z","title":"Continuous Markov processes and stochastic equations.Rend","venue":null,"work_id":"b8d7eb50-3c72-41f0-9fe7-c154c9be5e30","year":1955},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.834354Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:ac0376364fa04820d8900fa87a9698bebd20bc1fe40f58f5e484212a2d87d7fb","observation_id":"7cc56c65-3edd-4b77-a634-30fb202a1ebe","resolution":{"observed_at":"2026-08-14T15:10:53.160817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.111872Z","title":"Optimal stopping and free-boundary problems","venue":null,"work_id":"16878629-7b61-485a-909b-ab802253f6b9","year":2006},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.843042Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:83c09dfe13b6541f1eb619c1d92ffa79e13e0635bc6ab3e8a2a59846adf22312","observation_id":"2b85be1e-fe31-4a6c-8e77-86b397c5f8f1","resolution":{"observed_at":"2026-08-14T15:10:53.120538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.082636Z","title":null,"venue":null,"work_id":"2b52e834-d627-42b0-99ba-5cb822fb4fc5","year":2002},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.855943Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:65dd0906568191f18fc55788e8573a947aa3b6303d608f2769e3fc0e55cfdcf2","observation_id":"9a7cd374-117c-4ef5-b33f-9f920a0c3268","resolution":{"observed_at":"2026-08-14T15:10:53.093056Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.050943Z","title":null,"venue":null,"work_id":"4f5da3cc-4ad9-4426-aad8-1e01e551f095","year":2010},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.874162Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:e852d85cfe55d2412252cc51b269ec6011def5b738fbccdcb1064073619ecdd7","observation_id":"8bc3a76e-2f82-427e-beb6-4c8af3c993a3","resolution":{"observed_at":"2026-08-14T15:10:53.062064Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.017255Z","title":"Optimal dual martingales, their analysis, and application to new algorithms for Bermudan products","venue":null,"work_id":"5d12ef96-857a-472c-b64a-284167e3d9bb","year":2013},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.884682Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:ef4177a56e8943459b65e1fd834eec923104c00680f8c6b7df6f29e878e2bc3e","observation_id":"8fb0d0cd-1759-4362-8070-db6dfcc92409","resolution":{"observed_at":"2026-08-14T15:10:53.027034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:52.954435Z","title":"On Bermudan options","venue":null,"work_id":"a3a2a86a-5934-462c-9596-a606e33a9312","year":2002},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.894768Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:af61737d5c0b6294b5d83bc66c76ddf3c9213dafe046ab3a5789ba603b2215c0","observation_id":"9cb37c1c-b5e5-4e9f-85b2-7be329a16020","resolution":{"observed_at":"2026-08-14T15:10:52.966979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:52.924350Z","title":null,"venue":null,"work_id":"28224e9d-c294-4455-b17d-d07aa70895e3","year":2004},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.906573Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:d1c72247ab4b646e7a889e031e479eea712fa8cca948ba6b8762034695c05867","observation_id":"f1a23ca3-4af7-4af9-bc97-f2141d2b34f6","resolution":{"observed_at":"2026-08-14T15:10:52.934082Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.07469","last_updated":"2018-09-05T19:39:17Z","snapshot_observed_at":"2026-08-14T20:39:17.055940Z","submitted_at":"2017-08-24T15:50:24Z","title":"DGM: A deep learning algorithm for solving partial differential equations","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.07469","snapshot_observed_at":"2026-08-14T15:10:51.918797Z","title":"arXiv:1708.07469 (2017), 7 pages","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.918797Z"},"links":{"cited_paper":"/paper/1708.07469","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:6a9baf3aa245580028c33ca1ba61394ff6142b1ac9211983bb58555ad0645c56","observation_id":"c8e2818f-c561-438f-b381-34a183b3806c","resolution":{"observed_at":"2026-08-14T15:10:51.918797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:52.893727Z","title":"Stochastic gradient descent in continuous time","venue":null,"work_id":"480a8262-e2f3-41d9-928f-7c8632cce755","year":2017},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.931686Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:26bc2dc8fac6ada7fc38c8a6b66cb19e672930931e538cd2d425bc5323b41f36","observation_id":"e298b61b-4f3c-4c0e-8ec1-9f9bb8c857a6","resolution":{"observed_at":"2026-08-14T15:10:52.902551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:52.846524Z","title":"DGM: a deep learning algorithm for solving partial differential equations","venue":null,"work_id":"629ceb6b-88ba-46ce-ba1b-659f0932a694","year":2018},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.942891Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:c8c21f5e88ad40d613bea8e4f2d76a8781671a71f7c119c36bec80b4a3eee17e","observation_id":"fa7eaef6-f5e9-480f-98d1-af73a3d8e428","resolution":{"observed_at":"2026-08-14T15:10:52.854730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:52.810531Z","title":"Javascript options and implied volatility calculator","venue":null,"work_id":"f995de85-a9d8-4033-9f22-940ebdcd3a91","year":2019},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.952250Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:204f28bed9d0b80b48c3754a076c7663a53a1cabecbf0cd9209be343587502af","observation_id":"d76dcf30-9d2e-4b9b-bbc4-daec781b4138","resolution":{"observed_at":"2026-08-14T15:10:52.821329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1211.5802","last_updated":"2012-11-25T20:31:18Z","snapshot_observed_at":"2026-08-15T00:38:15.099740Z","submitted_at":"2012-11-25T20:31:18Z","title":"Random Stopping Times in Stopping Problems and Stopping Games","version":1},"cited_work":{"arxiv_id":"1211.5802","doi":null,"metadata_source":"pith","pith_arxiv_id":"1211.5802","snapshot_observed_at":"2026-08-14T15:10:52.117930Z","title":"Random Stopping Times in Stopping Problems and Stopping Games","venue":"math.PR","work_id":"eb6ac48f-d94e-4430-8468-1cc26ad4349a","year":2012},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.961619Z"},"links":{"cited_paper":"/paper/1211.5802","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:0db1f7f45734bec5f830e1bb48a6745e96b2cb34f201061f2966dc03dcfe312b","observation_id":"ba5a9326-4102-4e9f-99c0-1e4ca7ffcab7","resolution":{"observed_at":"2026-08-14T15:10:52.132793Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:52.772249Z","title":null,"venue":null,"work_id":"e7178242-5431-4f46-a388-2c7247393b15","year":1993},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.977253Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:0138294c613ba6f87350c93de6f4dd982af4fb9c89013caaf98c0ad953a8e8bd","observation_id":"10be08ad-e3e7-478c-be31-c9565790475f","resolution":{"observed_at":"2026-08-14T15:10:52.780173Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:52.732495Z","title":"N., and Van Roy, B","venue":null,"work_id":"533b3c0a-6719-4e7b-8e61-8208a140f649","year":1999},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.990273Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:8ffbd31e8f29e09660f7a40d3e30fb4d7710bc30b1f4c88595c00ba293b4dd30","observation_id":"9e358eac-a280-42e7-8d7e-d284e3c0f5b9","resolution":{"observed_at":"2026-08-14T15:10:52.740391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:52.692340Z","title":"N., and Van Roy, B","venue":null,"work_id":"eb9c1dec-1e0f-4b4d-a74c-7c78f98675bc","year":2001},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.997657Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:e784467db687766980f00fbbce064774e56ba6a770f777117e120a5d69e6eb23","observation_id":"86aea9d6-f417-4a5c-8cd2-b6bda842dd05","resolution":{"observed_at":"2026-08-14T15:10:52.700416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.06622","last_updated":"2018-09-22T17:05:56Z","snapshot_observed_at":"2026-08-14T18:50:49.434754Z","submitted_at":"2018-07-17T18:53:20Z","title":"Deep Learning-Based BSDE Solver for Libor Market Model with Application to Bermudan Swaption Pricing and Hedging","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.06622","snapshot_observed_at":"2026-08-14T15:10:52.007215Z","title":"Deep Learning- Based BSDE Solver for Libor Market Model with Application to Bermudan Swaption Pricing and Hedging","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:52.007215Z"},"links":{"cited_paper":"/paper/1807.06622","citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:353cf1897d2c55c3775cec6853c01760546b66e37f21359f30f81c4f58979f18","observation_id":"ae1b3505-8254-409c-91df-e382186eed56","resolution":{"observed_at":"2026-08-14T15:10:52.007215Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:10:53.598974Z","title":null,"venue":null,"work_id":"baf4f6bb-b052-406e-abf7-cbe51e23eefc","year":2006},"citing_paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning","version":3},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-14T15:10:51.636703Z"},"links":{"citing_paper":"/paper/1908.01602"},"observation_digest":"sha256:ee2ad0cd5908a324881c47e237de76a6d58efa8b45bc0b7e6e9e67ea5e84d6e5","observation_id":"73905b2a-c278-49e4-b83d-947b2ef9cc35","resolution":{"observed_at":"2026-08-14T15:10:53.605753Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.01602","last_updated":"2021-08-08T22:09:32Z","latest_version":3,"primary_category":"cs.CE","snapshot_observed_at":"2026-08-16T03:49:28.628548Z","submitted_at":"2019-08-05T13:11:28Z","title":"Solving high-dimensional optimal stopping problems using deep learning"},"reference_resolution":{"displayed":91,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":29,"verified_exact":7,"verified_fuzzy":54},"total_outbound_references":91},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 91 of 91 outbound references and 2 inbound Pith citation observations for arXiv:1908.01602."}