Group-invariant generative adversarial networks (GANs) are a type of GAN...
In this paper, we demonstrate the versatility of mean-field games (MFGs)...
We rigorously quantify the improvement in the sample complexity of
varia...
Lipschitz regularized f-divergences are constructed by imposing a bound ...
We propose a new family of regularized Rényi divergences parametrized no...
Generative adversarial networks (GANs), a class of distribution-learning...
Probabilistic graphical models are a fundamental tool in probabilistic
m...
We develop a general framework for constructing new information-theoreti...
Timely completion of design cycles for multiscale and multiphysics syste...
We present an information-based uncertainty quantification method for ge...
We derive a new variational formula for the Rényi family of divergences,...
In this work, we present methodologies for the quantification of confide...
We introduce the concept of a Graph-Informed Neural Network (GINN), a hy...
Variational representations of distances and divergences between
high-di...
Microscopic (pore-scale) properties of porous media affect and often
det...