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Ariel Neufeld

Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion

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Nov 05, 2024
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Universal approximation results for neural networks with non-polynomial activation function over non-compact domains

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Oct 23, 2024
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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 $L^p$-sense

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Sep 30, 2024
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Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks

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Sep 25, 2024
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Non-asymptotic estimates for accelerated high order Langevin Monte Carlo algorithms

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May 09, 2024
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Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs with infinite activity

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May 08, 2024
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Universal Approximation Property of Random Neural Networks

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Dec 20, 2023
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Neural networks can detect model-free static arbitrage strategies

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Jun 19, 2023
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Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient

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Oct 24, 2022
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Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty

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Sep 30, 2022
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