Advances in generative modeling and adversarial learning have given rise to renewed interest in differentiable two-players games, with much of the attention falling on generative adversarial networks (GANs). Solving these games introduces distinct challenges compared to the standard minimization tasks that the machine learning (ML) community is used to. A symptom of this issue is ML and deep learning (DL) practitioners using optimization tools on game-theoretic problems. Recent work seeks to rectify this situation by bringing game theoretic tools into ML. At NeurIPS 2018 we held "Smooth games optimization in ML", a workshop with this scope and goal in mind.