We address the problem of denoising data from a Gaussian mixture using a two-layer non-linear autoencoder with tied weights and a skip connection.

We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large graphs, with a focus on their expressive power.

Existing methods can be broadly divided into two categories:importance sampling(IS) based and model-based [58].

Unlikepriorworkon individual fairness, we do not assume the similarity measure among individuals is known, nor do we assume that such measure takes a certain parametric form.