Robust Utility Maximization in Discrete-Time Markets with Friction. - Ariel Neufeld; Mario Sikic (SIAM J. Control. Optim., 2018)

The Oracle of DLphi. - 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; et al. (CoRR, 2019)

Nonconcave robust optimization with discrete strategies under Knightian uncertainty. - Ariel Neufeld; Mario Sikic (Math. Methods Oper. Res., 2019)

Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. - Pushpendu Ghosh; Ariel Neufeld; Jajati Keshari Sahoo (CoRR, 2020)

Deep learning based numerical approximation algorithms for stochastic partial differential equations and high-dimensional nonlinear filtering problems. - Christian Beck; Sebastian Becker; Patrick Cheridito; Arnulf Jentzen; Ariel Neufeld (CoRR, 2020)

Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function. - Dong-Young Lim; Ariel Neufeld; Sotirios Sabanis; Ying Zhang 037 (CoRR, 2021)

Model-Free Price Bounds Under Dynamic Option Trading. - Ariel Neufeld; Julian Sester (SIAM J. Financial Math., 2021)

Deep Splitting Method for Parabolic PDEs. - Christian Beck; Sebastian Becker; Patrick Cheridito; Arnulf Jentzen; Ariel Neufeld (SIAM J. Sci. Comput., 2021)

Numerical method for feasible and approximately optimal solutions of multi-marginal optimal transport beyond discrete measures. - Ariel Neufeld; Qikun Xiang (CoRR, 2022)

Numerical method for approximately optimal solutions of two-stage distributionally robust optimization with marginal constraints. - Ariel Neufeld; Qikun Xiang (CoRR, 2022)

Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis. - Ariel Neufeld; Sizhou Wu (CoRR, 2022)

Markov Decision Processes under Model Uncertainty. - Ariel Neufeld; Julian Sester; Mario Sikic (CoRR, 2022)

Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting. - Ariel Neufeld; Matthew Ng Cheng En; Ying Zhang 037 (CoRR, 2022)

Chaotic Hedging with Iterated Integrals and Neural Networks. - Ariel Neufeld; Philipp Schmocker (CoRR, 2022)

Langevin dynamics based algorithm e-THεO POULA for stochastic optimization problems with discontinuous stochastic gradient. - Dong-Young Lim; Ariel Neufeld; Sotirios Sabanis; Ying Zhang 037 (CoRR, 2022)

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. - Yongming Li; Ariel Neufeld (CoRR, 2023)

Neural networks can detect model-free static arbitrage strategies. - Ariel Neufeld; Julian Sester (CoRR, 2023)

Feasible approximation of matching equilibria for large-scale matching for teams problems. - Ariel Neufeld; Qikun Xiang (CoRR, 2023)

Multilevel Picard algorithm for general semilinear parabolic PDEs with gradient-dependent nonlinearities. - Ariel Neufeld; Sizhou Wu (CoRR, 2023)

Deep ReLU neural networks overcome the curse of dimensionality when approximating semilinear partial integro-differential equations. - Ariel Neufeld; Tuan Anh Nguyen; Sizhou Wu (CoRR, 2023)

Rectified deep neural networks overcome the curse of dimensionality when approximating solutions of McKean-Vlasov stochastic differential equations. - Ariel Neufeld; Tuan Anh Nguyen (CoRR, 2023)

Universal Approximation Property of Random Neural Networks. - Ariel Neufeld; Philipp Schmocker (CoRR, 2023)

Low-Rank Plus Sparse Decomposition of Covariance Matrices Using Neural Network Parametrization. - Michel Baes; Calypso Herrera; Ariel Neufeld; Pierre Ruyssen (IEEE Trans. Neural Networks Learn. Syst., 2023)

Model-Free Bounds for Multi-Asset Options Using Option-Implied Information and Their Exact Computation. - Ariel Neufeld; Antonis Papapantoleon; Qikun Xiang (Manag. Sci., 2023)

A Deep Learning Approach to Data-Driven Model-Free Pricing and to Martingale Optimal Transport. - Ariel Neufeld; Julian Sester (IEEE Trans. Inf. Theory, 2023)

An efficient Monte Carlo scheme for Zakai equations. - Christian Beck; Sebastian Becker; Patrick Cheridito; Arnulf Jentzen; Ariel Neufeld (Commun. Nonlinear Sci. Numer. Simul., 2023)

Robust Q-learning algorithm for Markov decision processes under Wasserstein uncertainty. - Ariel Neufeld; Julian Sester (Autom., 2024)

Rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of gradient-dependent semilinear heat equations. - Ariel Neufeld; Tuan Anh Nguyen (CoRR, 2024)

Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis. - Daniel Bartl; Ariel Neufeld; Kyunghyun Park (CoRR, 2024)

Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs with infinite activity. - Ariel Neufeld; Philipp Schmocker; Sizhou Wu (CoRR, 2024)

Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks. - Luxu Liang; Ariel Neufeld; Ying Zhang (CoRR, 2024)

Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in Lp-sense. - Ariel Neufeld; Tuan Anh Nguyen (CoRR, 2024)

Universal approximation results for neural networks with non-polynomial activation function over non-compact domains. - Ariel Neufeld; Philipp Schmocker (CoRR, 2024)

Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion. - Ariel Neufeld; Philipp Schmocker (CoRR, 2024)

Binary spatial random field reconstruction from non-Gaussian inhomogeneous time-series observations. - Shunan Sheng; Qikun Xiang; Ido Nevat; Ariel Neufeld (J. Frankl. Inst., 2024)

Detecting Data-Driven Robust Statistical Arbitrage Strategies with Deep Neural Networks. - Ariel Neufeld; Julian Sester; Daiying Yin (SIAM J. Financial Math., 2024)

QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning. - Hari Hara Suthan Chittoor; Paul Robert Griffin; Ariel Neufeld; Jayne Thompson; Mile Gu (ICAART (1), 2025)

Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs. - Ariel Neufeld; Philipp Schmocker; Sizhou Wu (Commun. Nonlinear Sci. Numer. Simul., 2025)

Bounding the difference between the values of robust and non-robust Markov decision problems. - Ariel Neufeld; Julian Sester (J. Appl. Probab., 2025)

Multilevel Picard approximations overcome the curse of dimensionality in the numerical approximation of general semilinear PDEs with gradient-dependent nonlinearities. - Ariel Neufeld; Tuan Anh Nguyen; Sizhou Wu (J. Complex., 2025)

Multilevel Picard approximations for McKean-Vlasov stochastic differential equations with nonconstant diffusion. - Ariel Neufeld; Tuan Anh Nguyen; Philipp Schmocker (CoRR, 2025)

Provably convergent stochastic fixed-point algorithm for free-support Wasserstein barycenter of continuous non-parametric measures. - Zeyi Chen; Ariel Neufeld; Qikun Xiang (CoRR, 2025)