Contemporary work on learning in continuous games has commonly overlooked the
hierarchical decision-making structure present in machine learning problems formulated as …

Generative adversarial networks (GANs) represent a zero-sum game between two machine
players, a generator and a discriminator, designed to learn the distribution of data. While …

MRI synthesis promises to mitigate the challenge of missing MRI modality in clinical practice.
Diffusion model has emerged as an effective technique for image synthesis by modelling …

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 …

Many tasks in modern machine learning can be formulated as finding equilibria in\emph
{sequential} games. In particular, two-player zero-sum sequential games, also known as …

Algorithms for min-max optimization and variational inequalities are often studied under
monotonicity assumptions. Motivated by non-monotone machine learning applications, we …

This paper investigates the convergence of learning dynamics in Stackelberg games. In the
class of games we consider, there is a hierarchical game being played between a leader …

Generative adversarial networks (GANs) provide state-of-the-art results in image generation.
However, despite being so powerful, they still remain very challenging to train. This is in …

The existence of adversarial examples capable of fooling trained neural network classifiers
calls for a much better understanding of possible attacks to guide the development of …

Generative adversarial networks (GANs), a novel framework for training generative models
in an adversarial setup, have attracted significant attention in recent years. The two …