1 citations · 3 across the 4 of their papers we have counts for
6 papers
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…
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…
Low-Rank plus Sparse Decomposition of Covariance Matrices using Neural Network Parametrization
Michel Baes, Calypso Herrera, Ariel Neufeld +1
This paper revisits the problem of decomposing a positive semidefinite matrix as a sum of a matrix with a given rank plus a sparse matrix. An immediate application can be found in…
Parallel American Monte Carlo
Calypso Herrera, Louis Paulot
In this paper we introduce a new algorithm for American Monte Carlo that can be used either for American-style options, callable structured products or for computing counterparty c…