120 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- CY Cergy Paris UniversitéFR30 papers
- ESSEC Business SchoolSG11 papers
- Centre de Recherche en Économie et StatistiqueFR10 papers
- Johannes Kepler University of LinzAT4 papers
- Sorbonne UniversitéFR4 papers
- Université Paris CitéFR4 papers
- Centre for Research in Engineering Surface TechnologyIE3 papers
- Mathématiques Appliquées à Paris 5FR3 papers
- Center for Responsible TravelUS2 papers
- Decision Sciences (United States)US2 papers
- Département mathématiques, informatique, sciences de la donnée et technologies du numériqueFR2 papers
- ENSAE ParisFR2 papers
9 papers · 1 filter
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…
Learning with Importance Weighted Variational Inference
Kamélia Daudel, François Roueff
Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimization, such as the Importance-weighted Aut…
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
Otmane Sakhi, Imad Aouali, Pierre Alquier +1
This work investigates the offline formulation of the contextual bandit problem, where the goal is to leverage past interactions collected under a behavior policy to evaluate, sele…
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes
Charles Riou, Pierre Alquier, Badr-Eddine Chérief-Abdellatif
Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For example, the Gibbs algorithm with prior has an excess risk in , as oppos…
User-friendly introduction to PAC-Bayes bounds
Pierre Alquier
Aggregated predictors are obtained by making a set of basic predictors vote according to some weights, that is, to some probability distribution. Randomized predictors are obtained…
Outliers Detection in Networks with Missing Links
Solenne Gaucher, Olga Klopp, Geneviève Robin
Outliers arise in networks due to different reasons such as fraudulent behavior of malicious users or default in measurement instruments and can significantly impair network analys…
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach
Alexander Lin, Yingzhuo Zhang, Jeremy Heng +4
We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups. Our motivation comes f…
Collective Matrix Completion
Mokhtar Z. Alaya, Olga Klopp
Matrix completion aims to reconstruct a data matrix based on observations of a small number of its entries. Usually in matrix completion a single matrix is considered, which can be…
Bayesian matrix completion: prior specification
Pierre Alquier, Vincent Cottet, Nicolas Chopin +1
Low-rank matrix estimation from incomplete measurements recently received increased attention due to the emergence of several challenging applications, such as recommender systems;…