several formulations for meta-learning, but perhaps one of the most successful ones is the Model-Agnostic Meta-Learning (MAML) framework proposed in [@finn2017model]. In MAML, we aim to train the model parameters such that applying a few steps of gradient-based updates with a small number of samples from a new task would perform well on that task. MAML can also be viewed as a way to provide a proper initialization, from which performance on a new task can be optimized after a few gradient-based updates. Alas, this scheme only applies to settings in which the decision variable belongs to a *continuous* domain and can be adjusted using gradient-based methods at the test time.

Our goal is to extend the methodology of MAML to the *discrete* setting. We consider a setting in which our decision variable is a discrete set, and our goal is to come up with a good initial set that can be quickly adjusted to perform well over a wide range of new tasks. In particular, we focus on submodular maximization to represent the tasks which is an essential class of discrete optimization.

There are numerous applications where the submodular meta-learning framework can be applied to find a personalized solution 