7 citations · 13 across the 6 of their papers we have counts for
7 papers
The generalized lower triangular process prior
Antonio Canale, Sylvia Frühwirth-Schnatter
In the analysis of multivariate data, factor models represent a powerful technique for both reducing dimensionality and facilitating qualitative understanding of the latent determi…
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…
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…
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…
From NeurODEs to AutoencODEs: a mean-field control framework for width-varying Neural Networks
Cristina Cipriani, Massimo Fornasier, Alessandro Scagliotti
The connection between Residual Neural Networks (ResNets) and continuous-time control systems (known as NeurODEs) has led to a mathematical analysis of neural networks which has pr…
A Measure Theoretical Approach to the Mean-field Maximum Principle for Training NeurODEs
Benoît Bonnet, Cristina Cipriani, Massimo Fornasier +1
In this paper we consider a measure-theoretical formulation of the training of NeurODEs in the form of a mean-field optimal control with -regularization of the control. We der…
Zero-Inertia Limit: from Particle Swarm Optimization to Consensus Based Optimization
Cristina Cipriani, Hui Huang, Jinniao Qiu
Recently a continuous description of the particle swarm optimization (PSO) based on a system of stochastic differential equations was proposed by Grassi and Pareschi in arXiv:2012.…