The area of high-dimensional statistics concerns problems in which the ambient dimension is of the same order or substantially larger than the sample size. Although its roots are classical (dating back to Kolmogorov), it has become the focus of increasing attention in the modern era of big data. In this tutorial lecture, we survey some recent progress in different areas, including sparse estimation, covariance estimation, low-rank matrix recovery, graphical model selection, and non-parametric regression, with emphasis on the techniques used to obtain non-asymptotic results.

The first session of this talk will take place on Thursday, September 5 from 11:00 am – 12:30 pm.