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Robust Statistics is the study of designing estimators that perform
well even when the dataset significantly deviates from the
idealized modeling assumptions, such as model misspecification or
adversarial outliers in the dataset. The classical statistical
theory, dating back to pioneering works by Tukey and Huber,
characterizes the information-theoretic limits of robust estimation
for most common problems. A recent line of work in computer science
gave the first computationally efficient robust estimators in high
dimensions for a range of learning tasks. This reference text for
graduate students, researchers, and professionals in machine
learning theory, provides an overview of recent developments in
algorithmic high-dimensional robust statistics, presenting the
underlying ideas in a clear and unified manner, while leveraging
new perspectives on the developed techniques to provide streamlined
proofs of these results. The most basic and illustrative results
are analyzed in each chapter, while more tangential developments
are explored in the exercises.
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