Universal Robust Regression via Maximum Mean Discrepancy

Understand

Many modern datasets are collected automatically and are thus easily contaminated by outliers.

• This led to a regain of interest in robust estimation, including new notions of robustness such as robustness to adversarial contamination of the data.
• However, most robust estimation methods are designed for a specific model.
• Notably, many methods were proposed recently to obtain robust estimators in linear models (or generalized linear models), and a few were developed for very specific settings, for example beta regression or sample selection models.