A recent line of work in theoretical computer science obtained the first computationally
efficient robust estimators for a range of high-dimensional estimation tasks. In this tutorial
talk, we will survey the algorithmic techniques underlying these estimators and the connections
between them. We will illustrate these techniques for the following problems and settings:
robust mean and covariance estimation, robust stochastic optimization, robust estimation under sparsity assumptions,
list-decodable learning and mixture models, robust estimation of higher moments, computational-robustness tradeoffs.
Finally, we will discuss new directions and opportunities for future work.
|Time||Talk||2:00 - 3:15|| Ilias Diakonikolas|
Introduction; Robust Mean Estimation and Applications
|3:30- 4:30||Daniel Kane |
Four Vignettes: Robust Covariance Estimation, List-Decodable Learning, Robust Estimation of High-Degree Moments, Robust Sparse Estimation
|4:30- 5:00|| Ilias Diakonikolas |
Computational-Statistical Tradeoffs and Open Problems