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Found 8 papers, 7 papers with code
Robust Utility Optimization via a GAN Approach
Lastly, we uncover that our generative approach for learning optimal, (non-) robust investments under trading costs generates universally applicable alternatives to well known asymptotic strategies of idealized settings.
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing
We propose an optimal iterative scheme for federated transfer learning, where a central planner has access to datasets ${\cal D}_1,\dots,{\cal D}_N$ for the same learning model $f_{\theta}$.
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
The Path-Dependent Neural Jump Ordinary Differential Equation (PD-NJ-ODE) is a model for predicting continuous-time stochastic processes with irregular and incomplete observations.
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
This paper studies the problem of forecasting general stochastic processes using a path-dependent extension of the Neural Jump ODE (NJ-ODE) framework \citep{herrera2021neural}.
Optimal Stopping via Randomized Neural Networks
This paper presents the benefits of using randomized neural networks instead of standard basis functions or deep neural networks to approximate the solutions of optimal stopping problems.
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering
We introduce the Neural Jump ODE (NJ-ODE) that provides a data-driven approach to learn, continuously in time, the conditional expectation of a stochastic process.
Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices
The robust PCA of covariance matrices plays an essential role when isolating key explanatory features.
Local Lipschitz Bounds of Deep Neural Networks
The Lipschitz constant is an important quantity that arises in analysing the convergence of gradient-based optimization methods.
