6 papers
Quantitative stability in optimal transport for general power costs
Octave Mischler, Dario Trevisan
We establish novel quantitative stability results for optimal transport problems with respect to perturbations in the target measure. We provide explicit bounds on the stability of…
On distances among Slater Determinant States and Determinantal Point Processes
Chiara Boccato, Francesca Pieroni, Dario Trevisan
Determinantal processes provide mathematical modeling of repulsion among points. In quantum mechanics, Slater determinant states generate such processes, reflecting Fermionic behav…
Functional Large Deviations for Wide Deep Neural Networks with Gaussian Initialization and Lipschitz Activations
Claudio Macci, Barbara Pacchiarotti, Katerina Papagiannouli +2
We establish a functional large deviation principle for fully connected multi-layer perceptrons with i.i.d. Gaussian weights (LeCun initialization) and general Lipschitz activation…
Asymptotics for Random Quadratic Transportation Costs
Martin Huesmann, Michael Goldman, Dario Trevisan
We establish the validity of asymptotic limits for the general transportation problem between random i.i.d. points and their common distribution, with respect to the squared Euclid…
Student-t processes as infinite-width limits of posterior Bayesian neural networks
Francesco Caporali, Stefano Favaro, Dario Trevisan
The asymptotic properties of Bayesian Neural Networks (BNNs) have been extensively studied, particularly regarding their approximations by Gaussian processes in the infinite-width…
Wasserstein asymptotics for empirical measures of diffusions on four dimensional closed manifolds
Dario Trevisan, Feng-Yu Wang, Jie-Xiang Zhu
We identify the leading term in the asymptotics of the quadratic Wasserstein distance between the invariant measure and empirical measures for diffusion processes on closed weighte…