Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical
applications, such as personalized medicine and optimal resource allocation. In this article …

In many areas, practitioners seek to use observational data to learn a treatment assignment
policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or …

There has been considerable interest across several fields in methods that reduce the
problem of learning good treatment assignment policies to the problem of accurate policy …

Concentration inequalities deal with deviations of functions of independent random
variables from their expectation. In the last decade new tools have been introduced making …

This contribution develops a theoretical framework that takes into account the effect of
approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for …

Many of the classification algorithms developed in the machine learning literature, including
the support vector machine and boosting, can be viewed as minimum contrast methods that …

Birgé and Massart proposed in 2001 the slope heuristics as a way to choose optimally from
data an unknown multiplicative constant in front of a penalty. It is built upon the notion of …

The purpose of these lecture notes is to provide an introduction to the general theory of
empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities …

We propose new bounds on the error of learning algorithms in terms of a data-dependent
notion of complexity. The estimates we establish give optimal rates and are based on a local …