Solving high-dimensional optimal stopping problems using deep learning | Paper Citation Record

Observation 147e1ada-0c03-40f2-ad50-81b0128d955b · outbound

This paper cites American options: a comparison of numerical methods.

Solving high-dimensional optimal stopping problems using deep learning American options: a comparison of numerical methods

Reference 1

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Observation 2434c1c5-0685-4dd5-93a0-a0b64ec2ccf1 · outbound

This paper cites A simple approach to the pricing of bermudan swaptions in the multi-factor libor market model.

Solving high-dimensional optimal stopping problems using deep learning A simple approach to the pricing of bermudan swaptions in the multi-factor libor market model

Reference 2

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Observation 182c3207-a327-4e9c-8cb4-d67ab0e3fa9f · outbound

This paper cites Primal-dual simulation algorithm for pricing multidimensional american options.

Solving high-dimensional optimal stopping problems using deep learning Primal-dual simulation algorithm for pricing multidimensional american options

Reference 3

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This paper cites Error analysis of the optimal quantization algorithm for obstacle problems.

Solving high-dimensional optimal stopping problems using deep learning Error analysis of the optimal quantization algorithm for obstacle problems

Reference 4

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This paper cites Numerical valuation of high dimensional multivariate american securities.

Solving high-dimensional optimal stopping problems using deep learning Numerical valuation of high dimensional multivariate american securities

Reference 5

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Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 6

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This paper cites Implied stopping rules for American basket options from Markovian projection.

Solving high-dimensional optimal stopping problems using deep learning Implied stopping rules for American basket options from Markovian projection

Reference 7

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This paper cites Pricing American Options by Exercise Rate Optimization.

Solving high-dimensional optimal stopping problems using deep learning Pricing American Options by Exercise Rate Optimization

Reference 8

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This paper cites Deep optimal stopping.

Solving high-dimensional optimal stopping problems using deep learning Deep optimal stopping

Reference 9

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This paper cites Dynamic programming.

Solving high-dimensional optimal stopping problems using deep learning Dynamic programming

Reference 10

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Observation 1e4fee34-a5ef-464e-b690-d314992cd1c5 · outbound

This paper cites On the rates of convergence of simulation-based optimization algorithms for optimal stopping problems.

Solving high-dimensional optimal stopping problems using deep learning On the rates of convergence of simulation-based optimization algorithms for optimal stopping problems

Reference 11

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Observation dfdd09f4-f7e0-4657-ae9b-8da6a49a4d16 · outbound

This paper cites Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates.

Solving high-dimensional optimal stopping problems using deep learning Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates

Reference 12

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This paper cites Solving optimal stopping problems via empirical dual optimiza- tion.

Solving high-dimensional optimal stopping problems using deep learning Solving optimal stopping problems via empirical dual optimiza- tion

Reference 13

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Observation 861b9e81-a883-4a75-afcd-425a59a1ffbd · outbound

This paper cites True upper bounds for Bermudan products via non-nested Monte Carlo.

Solving high-dimensional optimal stopping problems using deep learning True upper bounds for Bermudan products via non-nested Monte Carlo

Reference 14

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Observation e357d850-2beb-4e5e-8d37-ad2ea53da272 · outbound

This paper cites Pricing Bermudan options via multilevel approximation methods.

Solving high-dimensional optimal stopping problems using deep learning Pricing Bermudan options via multilevel approximation methods

Reference 15

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This paper cites Multilevel simu- lation based policy iteration for optimal stopping—convergence and complexity.

Solving high-dimensional optimal stopping problems using deep learning Multilevel simu- lation based policy iteration for optimal stopping—convergence and complexity

Reference 16

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This paper cites Multilevel dual ap- proach for pricing American style derivatives.

Solving high-dimensional optimal stopping problems using deep learning Multilevel dual ap- proach for pricing American style derivatives

Reference 17

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This paper cites Policy iteration for Ameri- can options: overview.

Solving high-dimensional optimal stopping problems using deep learning Policy iteration for Ameri- can options: overview

Reference 18

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This paper cites Enhanced policy iteration for American options via scenario selection.

Solving high-dimensional optimal stopping problems using deep learning Enhanced policy iteration for American options via scenario selection

Reference 19

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Observation 69cb21f9-ca86-40bd-9ac8-4623a8b79b98 · outbound

This paper cites A primal-dual algorithm for BSDES.

Solving high-dimensional optimal stopping problems using deep learning A primal-dual algorithm for BSDES

Reference 20

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Observation c2066b53-e5c0-40c6-af60-7e722b617d16 · outbound

This paper cites J., and Schumacher, J.

Solving high-dimensional optimal stopping problems using deep learning J., and Schumacher, J

Reference 21

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Observation 177f2b31-9fb5-4b03-9618-94faf78bfa4f · outbound

This paper cites Discrete-time approximation and Monte-Carlo simulation of backward stochastic diﬀerential equations.

Solving high-dimensional optimal stopping problems using deep learning Discrete-time approximation and Monte-Carlo simulation of backward stochastic diﬀerential equations

Reference 22

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Observation f3dfc4e8-c00c-44b3-9d76-50df4be8c0d4 · outbound

This paper cites Improved lower and upper bound algorithms for pricing American options by simulation.

Solving high-dimensional optimal stopping problems using deep learning Improved lower and upper bound algorithms for pricing American options by simulation

Reference 23

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This paper cites Pricing American-style securities using sim- ulation.

Solving high-dimensional optimal stopping problems using deep learning Pricing American-style securities using sim- ulation

Reference 24

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Observation e90c67f2-9af2-4611-aedf-02e615141ae7 · outbound

This paper cites A stochastic mesh method for pricing high- dimensional American options.

Solving high-dimensional optimal stopping problems using deep learning A stochastic mesh method for pricing high- dimensional American options

Reference 25

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Observation 8caac4cf-3bd7-4cc5-b3e8-0ea43db87088 · outbound

This paper cites Pricing American options by sim- ulation using a stochastic mesh with optimized weights.

Solving high-dimensional optimal stopping problems using deep learning Pricing American options by sim- ulation using a stochastic mesh with optimized weights

Reference 26

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Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 27

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Observation ef6b2b72-2e8b-4d75-8a49-c0b6ce49b019 · outbound

This paper cites Additive and multiplicative duals for American option pricing.

Solving high-dimensional optimal stopping problems using deep learning Additive and multiplicative duals for American option pricing

Reference 28

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This paper cites A method for pricing American options using semi-inﬁnite linear programming.

Solving high-dimensional optimal stopping problems using deep learning A method for pricing American options using semi-inﬁnite linear programming

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:54.586048Z

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Observation 73d3389e-e0b1-40e3-9a3b-ee540ae7f43d · outbound

This paper cites Computing stable numerical solutions for multidimensional American option pricing problems: a semi-discretization approach.

Solving high-dimensional optimal stopping problems using deep learning Computing stable numerical solutions for multidimensional American option pricing problems: a semi-discretization approach

Reference 30

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This paper cites Approximation by superpositions of a sigmoidal function.

Solving high-dimensional optimal stopping problems using deep learning Approximation by superpositions of a sigmoidal function

Reference 31

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This paper cites Stochastic equations in inﬁnite dimensions , vol.

Solving high-dimensional optimal stopping problems using deep learning Stochastic equations in inﬁnite dimensions , vol

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Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 33

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This paper cites V., Farias, V.

Solving high-dimensional optimal stopping problems using deep learning V., Farias, V

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This paper cites Deep learning-based numerical methods for high-dimensional parabolic partial diﬀerential equations and backward stochastic dif- ferential equations.

Solving high-dimensional optimal stopping problems using deep learning Deep learning-based numerical methods for high-dimensional parabolic partial diﬀerential equations and backward stochastic dif- ferential equations

Reference 35

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Observation f1d764a1-e2fa-40a7-a23f-5f9bdaecb1d5 · outbound

This paper cites Monte Carlo algorithms for optimal stopping and statistical learning.

Solving high-dimensional optimal stopping problems using deep learning Monte Carlo algorithms for optimal stopping and statistical learning

Reference 36

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This paper cites A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options.

Solving high-dimensional optimal stopping problems using deep learning A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options

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Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 38

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Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 39

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This paper cites Asymptotic Expansion as Prior Knowledge in Deep Learning Method for High dimensional BSDEs.

Solving high-dimensional optimal stopping problems using deep learning Asymptotic Expansion as Prior Knowledge in Deep Learning Method for High dimensional BSDEs

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This paper cites Convergence and biases of Monte Carlo estimates of American option prices using a parametric exercise rule.

Solving high-dimensional optimal stopping problems using deep learning Convergence and biases of Monte Carlo estimates of American option prices using a parametric exercise rule

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This paper cites Monte Carlo methods in ﬁnancial engineering , vol.

Solving high-dimensional optimal stopping problems using deep learning Monte Carlo methods in ﬁnancial engineering , vol

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Observation e9fdd3bd-3bcf-47ba-ba06-fd0f2d336280 · outbound

This paper cites Understanding the diﬃculty of training deep feed- forward neural networks.

Solving high-dimensional optimal stopping problems using deep learning Understanding the diﬃculty of training deep feed- forward neural networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:54.080931Z

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Observation 2f2769b5-0565-4a99-9bf9-b30656537431 · outbound

This paper cites A regression-based Monte Carlo method to solve backward stochastic diﬀerential equations.

Solving high-dimensional optimal stopping problems using deep learning A regression-based Monte Carlo method to solve backward stochastic diﬀerential equations

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:54.036560Z

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Observation e585cd05-3961-4f15-b68b-ed8a10ef300b · outbound

This paper cites Polynomial time algorithm for optimal stopping with fixed accuracy.

Solving high-dimensional optimal stopping problems using deep learning Polynomial time algorithm for optimal stopping with fixed accuracy

Reference 45

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Observation 3701a09a-fd3d-4bf3-8149-075ac5cce6e0 · outbound

This paper cites Variance Reduction Applied to Machine Learning for Pricing Bermudan/American Options in High Dimension.

Solving high-dimensional optimal stopping problems using deep learning Variance Reduction Applied to Machine Learning for Pricing Bermudan/American Options in High Dimension

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Resolution: unresolved; no resolver link, observed 2026-08-14T15:10:51.487319Z

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This paper cites Nonlinear option pricing.

Solving high-dimensional optimal stopping problems using deep learning Nonlinear option pricing

Reference 47

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Observation 011dc44a-47de-49c8-bb93-3aacc8bd63d3 · outbound

This paper cites Solving high-dimensional partial diﬀerential equations using deep learning.

Solving high-dimensional optimal stopping problems using deep learning Solving high-dimensional partial diﬀerential equations using deep learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.958218Z

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This paper cites B., and Kogan, L.

Solving high-dimensional optimal stopping problems using deep learning B., and Kogan, L

Reference 49

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Observation 20196392-12f6-4e2a-bc08-d66bf0fcb362 · outbound

This paper cites Neural Networks 2 , 5 (1989), 359 – 366.

Solving high-dimensional optimal stopping problems using deep learning Neural Networks 2 , 5 (1989), 359 – 366

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.883528Z

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Observation bcf55e27-488d-473f-8537-85ec8efb0498 · outbound

This paper cites Batch normalization: accelerating deep network train- ing by reducing internal covariate shift.

Solving high-dimensional optimal stopping problems using deep learning Batch normalization: accelerating deep network train- ing by reducing internal covariate shift

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.845999Z

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Observation f004ad7a-8950-4b2c-8eee-7f51146265b8 · outbound

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Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 52

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Observation 1f2ef7c6-aa26-42b4-823e-382ea3b9217a · outbound

This paper cites The duality of optimal exercise and domineering claims: a Doob- Meyer decomposition approach to the Snell envelope.

Solving high-dimensional optimal stopping problems using deep learning The duality of optimal exercise and domineering claims: a Doob- Meyer decomposition approach to the Snell envelope

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.767462Z

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Observation 4c46d2ec-af1d-404d-820a-17625ea11a51 · outbound

This paper cites Strong convergence for explicit space-time discrete numerical approximation methods for stochastic Burgers equations.

Solving high-dimensional optimal stopping problems using deep learning Strong convergence for explicit space-time discrete numerical approximation methods for stochastic Burgers equations

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This paper cites R., and Powell, W.

Solving high-dimensional optimal stopping problems using deep learning R., and Powell, W

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.743045Z

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Observation deb408ed-0507-45dd-b7d7-a94e56661947 · outbound

This paper cites Option Pricing.

Solving high-dimensional optimal stopping problems using deep learning Option Pricing

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.722906Z

Source-reported events for the cited work

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Observation f5719683-60dd-40db-a3fd-f75f573af3b8 · outbound

This paper cites an unresolved cited work.

Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-14T15:10:51.601761Z

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Observation e03f42ac-9683-492c-9a5a-fcf8f34b591b · outbound

This paper cites Adam: a method for stochastic optimization.

Solving high-dimensional optimal stopping problems using deep learning Adam: a method for stochastic optimization

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.668627Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b5f8d6e-ead9-490a-a0ea-9755f7c2f049 · outbound

This paper cites Probability theory.

Solving high-dimensional optimal stopping problems using deep learning Probability theory

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.644952Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5c491ee8-571d-493a-9650-060b00abaaeb · outbound

This paper cites E., and Platen, E.

Solving high-dimensional optimal stopping problems using deep learning E., and Platen, E

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.580408Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 19e77252-db81-47dc-8a33-75e49a518fc4 · outbound

This paper cites A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time.

Solving high-dimensional optimal stopping problems using deep learning A regression-based smoothing spline Monte Carlo algorithm for pricing American options in discrete time

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.553638Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0f182dc2-8d97-4abb-b312-8ef4dd6d5a0c · outbound

This paper cites A review on regression-based Monte Carlo methods for pricing Amer- ican options.

Solving high-dimensional optimal stopping problems using deep learning A review on regression-based Monte Carlo methods for pricing Amer- ican options

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.508329Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ce3a4762-e999-4d5c-98db-7d77d6b4fbfe · outbound

This paper cites Pricing of American options in discrete time using least squares estimates with complexity penalties.

Solving high-dimensional optimal stopping problems using deep learning Pricing of American options in discrete time using least squares estimates with complexity penalties

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.477564Z

Source-reported events for the cited work

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Observation 6179c20b-2f76-41f3-95fe-b5f9126579da · outbound

This paper cites Pricing of high-dimensional American options by neural networks.

Solving high-dimensional optimal stopping problems using deep learning Pricing of high-dimensional American options by neural networks

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.443243Z

Source-reported events for the cited work

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Observation 4057f991-a731-43a6-80e1-d3afdc007bff · outbound

This paper cites Upper bounds for Bermudan options on Markovian data using nonparametric regression and a reduced number of nested Monte Carlo steps.

Solving high-dimensional optimal stopping problems using deep learning Upper bounds for Bermudan options on Markovian data using nonparametric regression and a reduced number of nested Monte Carlo steps

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.407289Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 68c39673-a250-4b5a-b031-37a4d01e327e · outbound

This paper cites Iterative construction of the optimal Bermudan stopping time.

Solving high-dimensional optimal stopping problems using deep learning Iterative construction of the optimal Bermudan stopping time

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.367035Z

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Observation efce755b-3b7e-4c11-8974-2a29bfaf0f3c · outbound

This paper cites V., and Gusyatnikov, P.

Solving high-dimensional optimal stopping problems using deep learning V., and Gusyatnikov, P

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.334195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 975f6a63-fa2f-4035-a348-7ef65649e6f0 · outbound

This paper cites A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options.

Solving high-dimensional optimal stopping problems using deep learning A Parallel Algorithm for solving BSDEs - Application to the pricing and hedging of American options

Reference 68

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T15:10:52.403209Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 225148a2-9b6b-401b-885b-5839b0bbaaec · outbound

This paper cites Introduction to stochastic calculus applied to ﬁnance, second ed.

Solving high-dimensional optimal stopping problems using deep learning Introduction to stochastic calculus applied to ﬁnance, second ed

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.305702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c7c39200-0c14-43ae-9482-ff81c36a5d98 · outbound

This paper cites Neural network regression for Bermudan option pricing.

Solving high-dimensional optimal stopping problems using deep learning Neural network regression for Bermudan option pricing

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:10:52.328291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e6dfe8fe-e22b-4b56-9a1a-1598269fe3bb · outbound

This paper cites Pricing American options using martingale bases.

Solving high-dimensional optimal stopping problems using deep learning Pricing American options using martingale bases

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:10:52.290930Z

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Observation 4df29bdb-cb1a-4c0c-9972-aac2c559b51f · outbound

This paper cites Pricing path-dependent Bermudan options using Wiener chaos expansion: an embarrassingly parallel approach.

Solving high-dimensional optimal stopping problems using deep learning Pricing path-dependent Bermudan options using Wiener chaos expansion: an embarrassingly parallel approach

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:10:52.232595Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7581aead-8578-47af-add8-e83ae44c744b · outbound

This paper cites A., and Schwartz, E.

Solving high-dimensional optimal stopping problems using deep learning A., and Schwartz, E

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.251761Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c0e5b9d0-4fcc-4d29-97d5-ec001462cad1 · outbound

This paper cites W.A fast and accurate FFT-based method for pricing early-exercise options under L´ evy processes.SIAM J.

Solving high-dimensional optimal stopping problems using deep learning W.A fast and accurate FFT-based method for pricing early-exercise options under L´ evy processes.SIAM J

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.203890Z

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Observation 7cc56c65-3edd-4b77-a634-30fb202a1ebe · outbound

This paper cites Continuous Markov processes and stochastic equations.Rend.

Solving high-dimensional optimal stopping problems using deep learning Continuous Markov processes and stochastic equations.Rend

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.160817Z

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Observation 2b85be1e-fe31-4a6c-8e77-86b397c5f8f1 · outbound

This paper cites Optimal stopping and free-boundary problems.

Solving high-dimensional optimal stopping problems using deep learning Optimal stopping and free-boundary problems

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.120538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9a7cd374-117c-4ef5-b33f-9f920a0c3268 · outbound

This paper cites an unresolved cited work.

Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:10:53.093056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8bc3a76e-2f82-427e-beb6-4c8af3c993a3 · outbound

This paper cites an unresolved cited work.

Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:10:53.062064Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8fb0d0cd-1759-4362-8070-db6dfcc92409 · outbound

This paper cites Optimal dual martingales, their analysis, and application to new algorithms for Bermudan products.

Solving high-dimensional optimal stopping problems using deep learning Optimal dual martingales, their analysis, and application to new algorithms for Bermudan products

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:53.027034Z

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Observation 9cb37c1c-b5e5-4e9f-85b2-7be329a16020 · outbound

This paper cites On Bermudan options.

Solving high-dimensional optimal stopping problems using deep learning On Bermudan options

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:52.966979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f1a23ca3-4af7-4af9-bc97-f2141d2b34f6 · outbound

This paper cites an unresolved cited work.

Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:10:52.934082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c8e2818f-c561-438f-b381-34a183b3806c · outbound

This paper cites DGM: A deep learning algorithm for solving partial differential equations.

Solving high-dimensional optimal stopping problems using deep learning DGM: A deep learning algorithm for solving partial differential equations

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-14T15:10:51.918797Z

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Observation e298b61b-4f3c-4c0e-8ec1-9f9bb8c857a6 · outbound

This paper cites Stochastic gradient descent in continuous time.

Solving high-dimensional optimal stopping problems using deep learning Stochastic gradient descent in continuous time

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:52.902551Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa7eaef6-f5e9-480f-98d1-af73a3d8e428 · outbound

This paper cites DGM: a deep learning algorithm for solving partial diﬀerential equations.

Solving high-dimensional optimal stopping problems using deep learning DGM: a deep learning algorithm for solving partial diﬀerential equations

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:52.854730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d76dcf30-9d2e-4b9b-bbc4-daec781b4138 · outbound

This paper cites Javascript options and implied volatility calculator.

Solving high-dimensional optimal stopping problems using deep learning Javascript options and implied volatility calculator

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:52.821329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ba5a9326-4102-4e9f-99c0-1e4ca7ffcab7 · outbound

This paper cites Random Stopping Times in Stopping Problems and Stopping Games.

Solving high-dimensional optimal stopping problems using deep learning Random Stopping Times in Stopping Problems and Stopping Games

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:10:52.132793Z

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Observation 10be08ad-e3e7-478c-be31-c9565790475f · outbound

This paper cites an unresolved cited work.

Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:10:52.780173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9e358eac-a280-42e7-8d7e-d284e3c0f5b9 · outbound

This paper cites N., and Van Roy, B.

Solving high-dimensional optimal stopping problems using deep learning N., and Van Roy, B

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:52.740391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 86aea9d6-f417-4a5c-8cd2-b6bda842dd05 · outbound

This paper cites N., and Van Roy, B.

Solving high-dimensional optimal stopping problems using deep learning N., and Van Roy, B

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:10:52.700416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae1b3505-8254-409c-91df-e382186eed56 · outbound

This paper cites Deep Learning-Based BSDE Solver for Libor Market Model with Application to Bermudan Swaption Pricing and Hedging.

Solving high-dimensional optimal stopping problems using deep learning Deep Learning-Based BSDE Solver for Libor Market Model with Application to Bermudan Swaption Pricing and Hedging

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-14T15:10:52.007215Z

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Unavailable: canonical work link unavailable.

Observation 73905b2a-c278-49e4-b83d-947b2ef9cc35 · outbound

This paper cites an unresolved cited work.

Solving high-dimensional optimal stopping problems using deep learning Unresolved cited work

Reference 2008

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:10:53.605753Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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