76 citations · 755 across the 50 of their papers we have counts for
26 papers · 1 filter
Variance-Aware Estimation of Kernel Mean Embedding
Geoffrey Wolfer, Pierre Alquier
An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded independently of the dimension…
Tight Risk Bound for High Dimensional Time Series Completion
Pierre Alquier, Nicolas Marie, Amélie Rosier
Initially designed for independent datas, low-rank matrix completion was successfully applied in many domains to the reconstruction of partially observed high-dimensional time seri…
Universal Robust Regression via Maximum Mean Discrepancy
Pierre Alquier, Mathieu Gerber
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 robus…
Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence
Badr-Eddine Chérief-Abdellatif, Pierre Alquier
Many works in statistics aim at designing a universal estimation procedure, that is, an estimator that would converge to the best approximation of the (unknown) data generating dis…
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
Badr-Eddine Chérief-Abdellatif, Pierre Alquier
In some misspecified settings, the posterior distribution in Bayesian statistics may lead to inconsistent estimates. To fix this issue, it has been suggested to replace the likelih…
High dimensional VAR with low rank transition
Pierre Alquier, Karine Bertin, Paul Doukhan +1
We propose a vector auto-regressive (VAR) model with a low-rank constraint on the transition matrix. This new model is well suited to predict high-dimensional series that are highl…