# SCH

## Seminar

### Penalized empirical risk minimization and sparse recovery problems

Seminar Room 1, Newton Institute

#### Abstract

A number of problems in regression and classification can be stated as penalized empirical risk minimization over a linear span or a convex hull of a given dictionary with convex loss and convex complexity penalty, such as, for instance, $\ell_1$-norm. We will discuss several oracle inequalities showing how the error of the solution of such problems depends on the "sparsity" of the problem and the "geometry" of the dictionary.

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