Large-scale pre-trained generative models are taking the world by storm, due to their abilities in generating creative content.

In this work, we propose data-efficient and interpretable machine learning models for learning to solve numeric planning tasks.

Lipschitzness, which we make precise in Section 2 and in Assumption 3.1.

Our preliminary results suggest that the regularized exponential mechanism can effectively emulate previous empirical and population risk bounds, negating the need for smoothness assumptions for algorithms with polynomial running time.