Interactive learning systems like self-driving cars, recommender systems, and large language model chatbots are becoming increasingly ubiquitous in everyday life. From a machine learning perspective, the key technical challenge underlying such systems is that rather than simple prediction on i.i.d. data, an interactive learner influences the distribution of inputs it sees via the choices it has made in the past, dramatically increasing the statistical and computational complexity of the problem. In this talk, we’ll focus on how to perform interactive imitation learning efficiently. We derive a unifying, game-theoretic framework for imitation learning and provide several efficient reductions to more tractable problems like supervised or online learning. We also consider sample efficiency, both in terms of expert demonstrations and learner-environment interactions and derive minimax-optimal and polynomial-time algorithms, respectively.