Diffusion models have recently emerged as a powerful framework for generative modeling.
They consist of a forward process that perturbs input data with Gaussian white noise and a …

We perform a systematic study of the approximation properties and optimization dynamics of
recurrent neural networks (RNNs) when applied to learn input-output relationships in …

Abstract Stein Variational Gradient Descent (SVGD) is an important alternative to the
Langevin-type algorithms for sampling from probability distributions of the form $\pi …

The seminal work of Cohen and Peng [CP15](STOC 2015) introduced Lewis weight
sampling to the theoretical computer science community, which yields fast row sampling …

In the paper, a survey of the main results concerning univariate and multivariate exponential
power (EP) distributions is given, with main attention paid to mixture representations of these …

The Fisher-Rao distance is the geodesic distance between probability distributions in a
statistical manifold equipped with the Fisher metric, which is a natural choice of Riemannian …

In this paper, we consider distributed detection of sparse stochastic signals with quantized
measurements. Assume that both the noise and the dominant elements in sparse signals …

This paper tackles the problem of defending a neural network against adversarial attacks
crafted with different norms (in particular $\ell_\infty $ and $\ell_2 $ bounded adversarial …

We study the $ p $-generalized probit regression model, which is a generalized linear model
for binary responses. It extends the standard probit model by replacing its link function, the …

Sparsity has long been a theoretical and practical signal property in applied mathematics
and is utilized as a crucial concept in signal/image processing applications such as …