One of the major recent advances in theoretical machine learning is the development of efficient learning algorithms for various high-dimensional statistical models. The Achilles heel of these algorithms is the assumption that the samples are precisely generated from the model. This assumption is crucial for the performance of these algorithms: even a very small fraction of outliers can completely compromise the algorithms' behavior.

Recent results in theoretical computer science have led to the development of the first computationally efficient robust estimators for a range of high-dimensional models. The goal of this tutorial is to introduce the machine learning community to the core insights and techniques in this area of algorithmic robust statistics, and discuss new directions and opportunities for future work.