3 papers
stat.ML2025
Robust Optimization in Causal Models and G-Causal Normalizing Flows
Gabriele Visentin, Patrick Cheridito
In this paper, we show that interventionally robust optimization problems in causal models are continuous under the -causal Wasserstein distance, but may be discontinuous under…
stat.ML2025
Computing Optimal Transport Maps and Wasserstein Barycenters Using Conditional Normalizing Flows
Gabriele Visentin, Patrick Cheridito
We present a novel method for efficiently computing optimal transport maps and Wasserstein barycenters in high-dimensional spaces. Our approach uses conditional normalizing flows t…
q-fin.RM2024
Conditional Forecasting of Margin Calls using Dynamic Graph Neural Networks
Matteo Citterio, Marco D'Errico, Gabriele Visentin
We introduce a novel Dynamic Graph Neural Network (DGNN) architecture for solving conditional -steps ahead forecasting problems in temporal financial networks. The proposed DGNN…