Convergent sequences of real numbers play a fundamental role in many different problems
in system theory, eg, in Lyapunov stability analysis, as well as in optimization theory and …

Similar to the role of Markov decision processes in reinforcement learning, Markov games
(also called stochastic games) lay down the foundation for the study of multi-agent …

In recent years, machine learning (ML) has been revolutionizing biology, biomedical
sciences, and gene‐based agricultural technology capabilities. Massive data generated in …

A significant robustness gap exists between machine intelligence and human perception
despite recent advances in deep learning. Deep learning is not provably secure. A critical …

Generative adversarial networks (GANs) are a class of generative models with two
antagonistic neural networks: a generator and a discriminator. These two neural networks …

This chapter summarizes the game theoretical strategies for generating adversarial
manipulations. The adversarial learning objective for our adversaries is assumed to be to …

In this paper, a distributed method is proposed for training multiple generative adversarial
networks (GANs) with private data sets via a game‐theoretic approach. To facilitate the …

Generalized Strategy Synthesis of Infinite-State Impartial Combinatorial Games via Exact Binary
Classification Page 1 Generalized Strategy Synthesis of Infinite-State Impartial Combinatorial …

Remote sensing image plays an important role in human activities and environmental
monitoring, and it is often necessary to perform super-resolution (SR) reconstruction for clear …

We study stochastic Nash equilibrium problems with expected valued cost functions whose
pseudogradient satisfies restricted monotonicity properties which hold only with respect to …