Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many
signal processing and machine learning (ML) applications. It is used for solving optimization …

Machine learning (ML) has been universally adopted for automated decisions in a variety of
fields, including recognition and classification applications, recommendation systems …

Data poisoning and backdoor attacks manipulate training data in order to cause models to
fail during inference. A recent survey of industry practitioners found that data poisoning is the …

The tremendous amount of accessible data in cyberspace face the risk of being
unauthorized used for training deep learning models. To address this concern, methods are …

In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they …

Large-scale training of modern deep learning models heavily relies on publicly available
data on the web. This potentially unauthorized usage of online data leads to concerns …

The min-max optimization problem, also known as the<; i> saddle point problem<;/i>, is a
classical optimization problem that is also studied in the context of zero-sum games. Given a …

Gradient descent ascent (GDA), the simplest single-loop algorithm for nonconvex minimax
optimization, is widely used in practical applications such as generative adversarial …

Abstract Stochastic Gradient Descent-Ascent (SGDA) is one of the most prominent
algorithms for solving min-max optimization and variational inequalities problems (VIP) …

Compared to minimization, the min-max optimization in machine learning applications is
considerably more convoluted because of the existence of cycles and similar phenomena …