121 citations · 377 across the 72 of their papers we have counts for
7 papers · 1 filter
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
William Andersson, Jakob Heiss, Florian Krach +1
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.…
Global universal approximation of functional input maps on weighted spaces
Christa Cuchiero, Philipp Schmocker, Josef Teichmann
We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space.…
Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
Florian Krach, Marc Nübel, Josef Teichmann
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}. Whi…
Optimal Stopping via Randomized Neural Networks
Calypso Herrera, Florian Krach, Pierre Ruyssen +1
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 pr…
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering
Calypso Herrera, Florian Krach, Josef Teichmann
Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outper…
Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices
Calypso Herrera, Florian Krach, Anastasis Kratsios +2
The robust PCA of covariance matrices plays an essential role when isolating key explanatory features. The currently available methods for performing such a low-rank plus sparse de…