research
∙
08/07/2023
Feasible approximation of matching equilibria for large-scale matching for teams problems
We propose a numerical algorithm for computing approximately optimal sol...
share
research
∙
06/19/2023
Neural networks can detect model-free static arbitrage strategies
In this paper we demonstrate both theoretically as well as numerically t...
research
∙
01/23/2023
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
In this paper we provide a quantum Monte Carlo algorithm to solve high-d...
research
∙
10/24/2022
An efficient Monte Carlo scheme for Zakai equations
In this paper we develop a numerical method for efficiently approximatin...
research
∙
10/24/2022
Langevin dynamics based algorithm e-THεO POULA for stochastic optimization problems with discontinuous stochastic gradient
We introduce a new Langevin dynamics based algorithm, called e-THεO POUL...
research
∙
09/30/2022
Robust Q-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty
We present a novel Q-learning algorithm to solve distributionally robust...
research
∙
09/21/2022
Chaotic Hedging with Iterated Integrals and Neural Networks
In this paper, we extend the Wiener-Ito chaos decomposition to the class...
research
∙
07/06/2022
Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting
We consider the problem of sampling from a high-dimensional target distr...
research
∙
06/13/2022
Markov Decision Processes under Model Uncertainty
We introduce a general framework for Markov decision problems under mode...
research
∙
05/19/2022
Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis
In this paper we introduce a multilevel Picard approximation algorithm f...
research
∙
05/11/2022
Numerical method for approximately optimal solutions of two-stage distributionally robust optimization with marginal constraints
We consider a general class of two-stage distributionally robust optimiz...
research
∙
04/07/2022
Binary Spatial Random Field Reconstruction from Non-Gaussian Inhomogeneous Time-series Observations
We develop a new model for binary spatial random field reconstruction of...
research
∙
03/07/2022
Detecting data-driven robust statistical arbitrage strategies with deep neural networks
We present an approach, based on deep neural networks, that allows ident...
research
∙
03/03/2022
Numerical method for feasible and approximately optimal solutions of multi-marginal optimal transport beyond discrete measures
We propose a numerical algorithm for the computation of multi-marginal o...
research
∙
07/19/2021
Non-asymptotic estimates for TUSLA algorithm for non-convex learning with applications to neural networks with ReLU activation function
We consider non-convex stochastic optimization problems where the object...
research
∙
03/21/2021
A deep learning approach to data-driven model-free pricing and to martingale optimal transport
We introduce a novel and highly tractable supervised learning approach b...
research
∙
12/02/2020
Deep learning based numerical approximation algorithms for stochastic partial differential equations and high-dimensional nonlinear filtering problems
In this article we introduce and study a deep learning based approximati...
research
∙
04/21/2020
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests
We employ both random forests and LSTM networks (more precisely CuDNNLST...
research
∙
08/01/2019
Low-Rank plus Sparse Decomposition of Covariance Matrices using Neural Network Parametrization
This paper revisits the problem of decomposing a positive semidefinite m...
research
∙
07/08/2019
Deep splitting method for parabolic PDEs
In this paper we introduce a numerical method for parabolic PDEs that co...
research
∙
01/17/2019
