How (not) to Model an Adversary (Ruth Urner)

Statistical learning (and theory) traditionally relies on training and test data being generated by the same process, an assumption that rarely holds in practice. Conditions of data-generation might change over time, or agents might (strategically or adversarially) respond to a published predictor aiming for a specific outcome for their manipulated instance. Developing methods for adversarial robustness has received a lot of attention in recent years, and both practical tools and theoretical guarantees developed. In this talk, I will focus on the learning theoretic treatment of these scenarios and survey how different modeling assumptions can lead to drastically different conclusions. I will argue that for robustness we should aim for minimal assumptions on how an adversary might act, and present recent results on a variety of relaxations of learning with standard adversarial (or strategic) robustness.