Parametric estimation via MMD optimization: robustness to outliers and dependence
Pierre is a research scientist at RIKEN Center for Advanced Intelligence Project (AIP). Prior to that he has worked as a lecturer at the Université de Paris and UCD Dublin, and as a professor in statistics at ENSAE Paris. His research interests include high-dimensional statistics, statistical learning theory, and frequentist study of Bayesian methods, including PAC-Bayes bounds.
In this talk, I will study the properties of parametric estimators based on the Maximum Mean Discrepancy (MMD) defined by Briol et al. (2019). In a first time, I will show that these estimators are universal in the i.i.d setting: even in case of misspecification, they converge to the best approximation of the distribution of the data in the model, without ANY assumption on this model. This leads to very strong robustness properties. In a second time, I will show that these results remain valid when the data is not independent, but satisfy instead a weak-dependence condition. This condition is based on a new dependence coefficient, which is itself defined thanks to the MMD. I will show through examples that this new notion of dependence is actually quite general. This talk is based on published works, and works in progress, with Badr-Eddine Chérief Abdellatif (ENSAE Paris), Mathieu Gerber (University of Bristol), Jean-David Fermanian (ENSAE Paris) and Alexis Derumigny (University of Twente):