3 papers
cs.LG2026
Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning
Mahmoud Selim, Cristina Cipriani, Karl H. Johansson
Diffusion policies offer a powerful and expressive parameterization for continuous control. Yet, their integration with reinforcement learning remains conceptually and algorithmica…
cs.LG2024
The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks
Andrea Bonfanti, Giuseppe Bruno, Cristina Cipriani
The Neural Tangent Kernel (NTK) viewpoint is widely employed to analyze the training dynamics of overparameterized Physics-Informed Neural Networks (PINNs). However, unlike the cas…
math.OC2023
A minimax optimal control approach for robust neural ODEs
Cristina Cipriani, Alessandro Scagliotti, Tobias Wöhrer
In this paper, we address the adversarial training of neural ODEs from a robust control perspective. This is an alternative to the classical training via empirical risk minimizatio…