Ariel Neufeld

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 40

Top Topics

Robust Optimization

Differential Equations

Markov Decision Process

Top Venues

SIAM J. Financial Math.

IEEE Trans. Inf. Theory

Publications

Ariel Neufeld, Julian Sester
Robust Q-learning algorithm for Markov decision processes under Wasserstein uncertainty. Autom. 168 (2024)
Ariel Neufeld, Philipp Schmocker, Sizhou Wu
Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs with infinite activity. CoRR (2024)
Shunan Sheng, Qikun Xiang, Ido Nevat, Ariel Neufeld
Binary spatial random field reconstruction from non-Gaussian inhomogeneous time-series observations. J. Frankl. Inst. 361 (2) (2024)
Ariel Neufeld, Julian Sester, Daiying Yin
Detecting Data-Driven Robust Statistical Arbitrage Strategies with Deep Neural Networks. SIAM J. Financial Math. 15 (2) (2024)
Ariel Neufeld, Tuan Anh Nguyen
Rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of gradient-dependent semilinear heat equations. CoRR (2024)
Daniel Bartl, Ariel Neufeld, Kyunghyun Park
Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis. CoRR (2024)
Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld
An efficient Monte Carlo scheme for Zakai equations. Commun. Nonlinear Sci. Numer. Simul. 126 (2023)
Ariel Neufeld, Qikun Xiang
Feasible approximation of matching equilibria for large-scale matching for teams problems. CoRR (2023)
Ariel Neufeld, Tuan Anh Nguyen
Rectified deep neural networks overcome the curse of dimensionality when approximating solutions of McKean-Vlasov stochastic differential equations. CoRR (2023)
Ariel Neufeld, Sizhou Wu
Multilevel Picard algorithm for general semilinear parabolic PDEs with gradient-dependent nonlinearities. CoRR (2023)
Ariel Neufeld, Tuan Anh Nguyen, Sizhou Wu
Deep ReLU neural networks overcome the curse of dimensionality when approximating semilinear partial integro-differential equations. CoRR (2023)
Ariel Neufeld, Julian Sester
Neural networks can detect model-free static arbitrage strategies. CoRR (2023)
Ariel Neufeld, Philipp Schmocker
Universal Approximation Property of Random Neural Networks. CoRR (2023)
Ariel Neufeld, Julian Sester
A Deep Learning Approach to Data-Driven Model-Free Pricing and to Martingale Optimal Transport. IEEE Trans. Inf. Theory 69 (5) (2023)
Yongming Li, Ariel Neufeld
Quantum Monte Carlo algorithm for solving Black-Scholes PDEs for high-dimensional option pricing in finance and its proof of overcoming the curse of dimensionality. CoRR (2023)
Ariel Neufeld, Antonis Papapantoleon, Qikun Xiang
Model-Free Bounds for Multi-Asset Options Using Option-Implied Information and Their Exact Computation. Manag. Sci. 69 (4) (2023)
Ariel Neufeld, Tuan Anh Nguyen, Sizhou Wu
Multilevel Picard approximations overcome the curse of dimensionality in the numerical approximation of general semilinear PDEs with gradient-dependent nonlinearities. CoRR (2023)
Michel Baes, Calypso Herrera, Ariel Neufeld, Pierre Ruyssen
Low-Rank Plus Sparse Decomposition of Covariance Matrices Using Neural Network Parametrization. IEEE Trans. Neural Networks Learn. Syst. 34 (1) (2023)
Ariel Neufeld, Philipp Schmocker
Chaotic Hedging with Iterated Integrals and Neural Networks. CoRR (2022)
Ariel Neufeld, Julian Sester, Mario Sikic
Markov Decision Processes under Model Uncertainty. CoRR (2022)
Dong-Young Lim, Ariel Neufeld, Sotirios Sabanis, Ying Zhang
Langevin dynamics based algorithm e-THεO POULA for stochastic optimization problems with discontinuous stochastic gradient. CoRR (2022)
Ariel Neufeld, Qikun Xiang
Numerical method for feasible and approximately optimal solutions of multi-marginal optimal transport beyond discrete measures. CoRR (2022)
Shunan Sheng, Qikun Xiang, Ido Nevat, Ariel Neufeld
Binary Spatial Random Field Reconstruction from Non-Gaussian Inhomogeneous Time-series Observations. CoRR (2022)
Ariel Neufeld, Matthew Ng Cheng En, Ying Zhang
Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting. CoRR (2022)
Ariel Neufeld, Sizhou Wu
Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis. CoRR (2022)
Ariel Neufeld, Qikun Xiang
Numerical method for approximately optimal solutions of two-stage distributionally robust optimization with marginal constraints. CoRR (2022)
Ariel Neufeld, Julian Sester, Daiying Yin
Detecting data-driven robust statistical arbitrage strategies with deep neural networks. CoRR (2022)
Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld
An efficient Monte Carlo scheme for Zakai equations. CoRR (2022)
Ariel Neufeld, Julian Sester
Robust Q-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty. CoRR (2022)
Ariel Neufeld, Julian Sester
Model-Free Price Bounds Under Dynamic Option Trading. SIAM J. Financial Math. 12 (4) (2021)
Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld
Deep Splitting Method for Parabolic PDEs. SIAM J. Sci. Comput. 43 (5) (2021)
Dong-Young Lim, Ariel Neufeld, Sotirios Sabanis, Ying Zhang
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function. CoRR (2021)
Ariel Neufeld, Julian Sester
A deep learning approach to data-driven model-free pricing and to martingale optimal transport. CoRR (2021)
Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld
Deep learning based numerical approximation algorithms for stochastic partial differential equations and high-dimensional nonlinear filtering problems. CoRR (2020)
Pushpendu Ghosh, Ariel Neufeld, Jajati Keshari Sahoo
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. CoRR (2020)
Dominik Alfke, Weston Baines, Jan Blechschmidt, Mauricio J. del Razo Sarmina, Amnon Drory, Dennis Elbrächter, Nando Farchmin, Matteo Gambara, Silke Glas, Philipp Grohs, Peter Hinz, Danijel Kivaranovic, Christian Kümmerle, Gitta Kutyniok, Sebastian Lunz, Jan MacDonald, Ryan Malthaner, Gregory Naisat, Ariel Neufeld, Philipp Christian Petersen, Rafael Reisenhofer, Jun-Da Sheng, Laura Thesing, Philipp Trunschke, Johannes von Lindheim, David Weber, Melanie Weber
The Oracle of DLphi. CoRR (2019)
Ariel Neufeld, Mario Sikic
Nonconcave robust optimization with discrete strategies under Knightian uncertainty. Math. Methods Oper. Res. 90 (2) (2019)
Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld
Deep splitting method for parabolic PDEs. CoRR (2019)
Michel Baes, Calypso Herrera, Ariel Neufeld, Pierre Ruyssen
Low-Rank plus Sparse Decomposition of Covariance Matrices using Neural Network Parametrization. CoRR (2019)
Ariel Neufeld, Mario Sikic
Robust Utility Maximization in Discrete-Time Markets with Friction. SIAM J. Control. Optim. 56 (3) (2018)