Description and Outline
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.
- Part 1: Introduction and Robust Mean Estimation [pdf]
- Part 2: Basic Algorithmic Techniques [pdf]
- Part 3: Experiments and Extensions [pdf]
- Part 4: Computational-Statistical Tradeoffs and Research Directions [pdf]