1 citations · 3 across the 9 of their papers we have counts for
9 papers
Operator Neural Jump ODEs: -optimal prediction in function spaces
Florian Krach, Oliver Löthgren, Josef Teichmann
In this paper, we study the extension of Neural Jump ODEs to infinite-dimensional function spaces. In particular, the underlying process now takes values in $L^2(Ξ, \mathbb{R}^…
Neural Jump ODEs as Generative Models
Robert A. Crowell, Florian Krach, Josef Teichmann
In this work, we explore how Neural Jump ODEs (NJODEs) can be used as generative models for Itô processes. Given (discrete observations of) samples of a fixed underlying Itô proces…
Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
Florian Krach, Josef Teichmann
The Path-dependent Neural Jump ODE (PD-NJ-ODE) is a model for online prediction of generic (possibly non-Markovian) stochastic processes with irregular (in time) and potentially in…
Robust Utility Optimization via a GAN Approach
Florian Krach, Josef Teichmann, Hanna Wutte
Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a…
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.…
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…
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…
Local Lipschitz Bounds of Deep Neural Networks
Calypso Herrera, Florian Krach, Josef Teichmann
The Lipschitz constant is an important quantity that arises in analysing the convergence of gradient-based optimization methods. It is generally unclear how to estimate the Lipschi…