Section: Research Program

Estimator selection

An important goal of Celeste is to build and study procedures that can deal with general estimators (especially those actually used in practice, which often rely on some optimization algorithm), such as cross-validation and Lepski's method. In order to be practical, estimator selection procedures must be fully data-driven (that is, not relying on any unknown quantity), computationally tractable (especially in the high-dimensional setting, for which specific procedures must be developed) and robust to outliers (since most real data sets include a few outliers). Celeste aims at providing a precise theoretical analysis (for new and existing popular estimator selection procedures), that explains as well as possible their observed behaviour in practice.