Compressed sensing (CS) provides an elegant framework for recovering sparse signals
from compressed measurements. For example, CS can exploit the structure of natural …

Generative adversarial networks (GANs) have achieved significant success in generating
real-valued data. However, the discrete nature of text hinders the application of GAN to text …

Real-world datasets are often biased with respect to key demographic factors such as race
and gender. Due to the latent nature of the underlying factors, detecting and mitigating bias …

A learned generative model often produces biased statistics relative to the underlying data
distribution. A standard technique to correct this bias is importance sampling, where …

A fundamental challenge for machine learning models is how to generalize learned models
for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features …

Despite its short history, Generative Adversarial Network (GAN) has been extensively
studied and used for various tasks, including its original purpose, ie, synthetic sample …

The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily
life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have …

Contrastive representation learning seeks to acquire useful representations by estimating
the shared information between multiple views of data. Here, the choice of data …

Significant advances in deep learning have led to more widely used and precise neural
network-based generative models such as Generative Adversarial Networks (GANS). We …

Unsupervised image-to-image translation aims at learning the mapping from the source to
target domain without using paired images for training. An essential yet restrictive …