Abstract: The analysis of data from fundamental physics experiments such as the Large Hadron Collider often rely heavily on simulation. While state-of-the-art simulations are excellent and can describe a wide range of physical processes, they are often approximations to nature and have components that are empirical models. With a growing interest in using deep learning to extract the most information from our data, there is a need to ensure that our techniques are robust to mist-modeling. This is particularly important for high-dimensional learning where the key information can be distributed in subtle correlations across many dimensions of the feature space. I will give examples of how modern machine learning can be combined with physical insight to render classification, regression, generation, and anomaly detection models robust to mismodeling. I will use examples from LHC physics, but many of the methods have much broader applicability.