Ariel Neufeld
Orcid: 0000-0001-5500-5245
According to our database1,
Ariel Neufeld
authored at least 32 papers
between 2018 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2024
Binary spatial random field reconstruction from non-Gaussian inhomogeneous time-series observations.
J. Frankl. Inst., 2024
Rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of gradient-dependent semilinear heat equations.
CoRR, 2024
2023
Commun. Nonlinear Sci. Numer. Simul., November, 2023
A Deep Learning Approach to Data-Driven Model-Free Pricing and to Martingale Optimal Transport.
IEEE Trans. Inf. Theory, May, 2023
Model-Free Bounds for Multi-Asset Options Using Option-Implied Information and Their Exact Computation.
Manag. Sci., April, 2023
Low-Rank Plus Sparse Decomposition of Covariance Matrices Using Neural Network Parametrization.
IEEE Trans. Neural Networks Learn. Syst., 2023
Rectified deep neural networks overcome the curse of dimensionality when approximating solutions of McKean-Vlasov stochastic differential equations.
CoRR, 2023
Multilevel Picard approximations overcome the curse of dimensionality in the numerical approximation of general semilinear PDEs with gradient-dependent nonlinearities.
CoRR, 2023
Deep ReLU neural networks overcome the curse of dimensionality when approximating semilinear partial integro-differential equations.
CoRR, 2023
Multilevel Picard algorithm for general semilinear parabolic PDEs with gradient-dependent nonlinearities.
CoRR, 2023
Feasible approximation of matching equilibria for large-scale matching for teams problems.
CoRR, 2023
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
2022
Langevin dynamics based algorithm e-THεO POULA for stochastic optimization problems with discontinuous stochastic gradient.
CoRR, 2022
Robust Q-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty.
CoRR, 2022
Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting.
CoRR, 2022
Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis.
CoRR, 2022
Numerical method for approximately optimal solutions of two-stage distributionally robust optimization with marginal constraints.
CoRR, 2022
Detecting data-driven robust statistical arbitrage strategies with deep neural networks.
CoRR, 2022
Numerical method for feasible and approximately optimal solutions of multi-marginal optimal transport beyond discrete measures.
CoRR, 2022
2021
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function.
CoRR, 2021
2020
Deep learning based numerical approximation algorithms for stochastic partial differential equations and high-dimensional nonlinear filtering problems.
CoRR, 2020
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests.
CoRR, 2020
2019
Math. Methods Oper. Res., 2019
2018
SIAM J. Control. Optim., 2018