In recent years, adversarial learning methods are shown to be a key technique that leads to exciting breakthroughs and new challenges of many machine learning and data mining tasks. Examples include improved training of generative models (e.g., generative adversarial nets), adversarial robustness of machine learning systems in different domains (e.g., adversarial attacks, defenses, and property verification), and robust representation learning (e.g., adversarial loss for learning embedding), to name a few. Generally speaking, the idea of “learning with an adversary” is crucial for expanding the learning capability, ensuring trustworthy decision making, and enhancing generalizability of machine learning and data mining methods. This workshop also aims to bridge theory and practice by encouraging theoretical studies motivated by adversarial ML/DM problems, such as robust (minimax) optimization and game theory. The program of this workshop will include: (i) invited talks covering different aspects and recent advances of adversarial learning methods, and (ii) open call track for paper submissions.