2 papers
stat.ML2026
Beyond Lipschitz: Data-Driven Robustness via Discrete Modulus of Continuity
Jürgen Dölz, Michael Multerer, Michele Palma
Robustness of neural networks is commonly quantified via local or global Lipschitz constants. However, Lipschitz continuity can be overly coarse or overly restrictive as global rob…
q-fin.CP2024
Geometric Deep Learning for Realized Covariance Matrix Forecasting
Andrea Bucci, Michele Palma, Chao Zhang
Traditional methods employed in matrix volatility forecasting often overlook the inherent Riemannian manifold structure of symmetric positive definite matrices, treating them as el…