36-762 Advanced Theory I

6 units, Carnegie Mellon University

Outliers constitute a major challenge in any statistical estimation procedure: a tiny fraction of outliers in the training data could considerably change the algorithm's output. As the dimensionality of the data increases, tackling outliers becomes increasingly more challenging. This course will be an introduction to the field of robust statistics, which aims to develop statistical methods that are robust to tiny contaminations in the data. Our primary focus will be on the statistical and computational challenges that arise when the dimensionality of the data increases. This course will be theory-oriented. Prerequisites: concentration inequalities, linear algebra, and convex optimization. References: (i) Robust Statistics by P. Huber and E. Ronchetti and (ii) Algorithmic High-Dimensional Robust Statistics by Ilias Diakonikolas and Daniel M. Kane

Prerequisites: none listed

Offered: Spring 2026

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