Estimating Full Lipschitz Constants of Deep Neural Networks. - Calypso Herrera; Florian Krach; Josef Teichmann (CoRR, 2020)

Denise: Deep Learning based Robust PCA for Positive Semidefinite Matrices. - Calypso Herrera; Florian Krach; Josef Teichmann (CoRR, 2020)

Theoretical Guarantees for Learning Conditional Expectation using Controlled ODE-RNN. - Calypso Herrera; Florian Krach; Josef Teichmann (CoRR, 2020)

Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering. - Calypso Herrera; Florian Krach; Josef Teichmann (ICLR, 2021)

Optimal Stopping via Randomized Neural Networks. - Calypso Herrera; Florian Krach; Pierre Ruyssen; Josef Teichmann (CoRR, 2021)

Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices. - Calypso Herrera; Florian Krach; Anastasis Kratsios; Pierre Ruyssen; Josef Teichmann (Trans. Mach. Learn. Res., 2023)

Low-Rank Plus Sparse Decomposition of Covariance Matrices Using Neural Network Parametrization. - Michel Baes; Calypso Herrera; Ariel Neufeld; Pierre Ruyssen (IEEE Trans. Neural Networks Learn. Syst., 2023)