3 papers
stat.ML2025
Empirical PAC-Bayes Bounds for Markov Chains
Vahe Karagulyan, Pierre Alquier
The core of generalization theory was developed for independent observations. Some PAC and PAC-Bayes bounds are available for data that exhibit a temporal dependence. However, ther…
stat.CO2025
regMMD: An R package for parametric estimation and regression with maximum mean discrepancy
Pierre Alquier, Mathieu Gerber
The Maximum Mean Discrepancy (MMD) is a kernel-based metric widely used for nonparametric tests and estimation. Recently, it has also been studied as an objective function for para…
stat.ML2024
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Pierre Alquier, William Kengne
In a groundbreaking work, Schmidt-Hieber (2020) proved the minimax optimality of deep neural networks with ReLu activation for least-square regression estimation over a large class…