An Algorithm for Iterative Selection of Blocks of Features. - Pierre Alquier (ALT, 2010)

Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting. - Pierre Alquier; Xiaoyin Li (Discovery Science, 2012)

Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study. - Pierre Alquier (ALT, 2013)

Sparse single-index model. - Pierre Alquier; Gérard Biau (J. Mach. Learn. Res., 2013)

PAC-Bayesian AUC classification and scoring. - James Ridgway; Pierre Alquier; Nicolas Chopin; Feng Liang (NIPS, 2014)

On the properties of variational approximations of Gibbs posteriors. - Pierre Alquier; James Ridgway; Nicolas Chopin (J. Mach. Learn. Res., 2016)

Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels. - Pierre Alquier; Nial Friel; Richard G. Everitt; Aidan Boland (Stat. Comput., 2016)

Regret Bounds for Lifelong Learning. - Pierre Alquier; The Tien Mai; Massimiliano Pontil (AISTATS, 2017)

Non-negative matrix factorization as a pre-processing tool for travelers temporal profiles clustering. - Léna Carel; Pierre Alquier (ESANN, 2017)

Concentration of tempered posteriors and of their variational approximations. - Pierre Alquier; James Ridgway (CoRR, 2017)

Exponential inequalities for nonstationary Markov Chains. - Pierre Alquier; Paul Doukhan; Xiequan Fan (CoRR, 2018)

Simpler PAC-Bayesian bounds for hostile data. - Pierre Alquier; Benjamin Guedj (Mach. Learn., 2018)

1-Bit matrix completion: PAC-Bayesian analysis of a variational approximation. - Vincent Cottet; Pierre Alquier (Mach. Learn., 2018)

MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy. - Badr-Eddine Chérief-Abdellatif; Pierre Alquier (AABI, 2019)

A Generalization Bound for Online Variational Inference. - Badr-Eddine Chérief-Abdellatif; Pierre Alquier; Mohammad Emtiyaz Khan (ACML, 2019)

Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence. - Badr-Eddine Chérief-Abdellatif; Pierre Alquier (CoRR, 2019)

Informed sub-sampling MCMC: approximate Bayesian inference for large datasets. - Florian Maire; Nial Friel; Pierre Alquier (Stat. Comput., 2019)

Approximate Bayesian Inference. - Pierre Alquier (Entropy, 2020)

High-dimensional VAR with low-rank transition. - Pierre Alquier; Karine Bertin; Paul Doukhan; Rémy Garnier (Stat. Comput., 2020)

A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. - Thang Doan; Mehdi Abbana Bennani; Bogdan Mazoure; Guillaume Rabusseau; Pierre Alquier (AISTATS, 2021)

Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions. - Pierre Alquier (ICML, 2021)

Simultaneous dimension reduction and clustering via the NMF-EM algorithm. - Léna Carel; Pierre Alquier (Adv. Data Anal. Classif., 2021)

Meta-Strategy for Learning Tuning Parameters with Guarantees. - Dimitri Meunier; Pierre Alquier (Entropy, 2021)

Variance-Aware Estimation of Kernel Mean Embedding. - Geoffrey Wolfer; Pierre Alquier (CoRR, 2022)

PAC-Bayesian Offline Contextual Bandits With Guarantees. - Otmane Sakhi; Pierre Alquier; Nicolas Chopin (ICML, 2023)

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 (CoRR, 2023)

Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning. - Otmane Sakhi; Imad Aouali; Pierre Alquier; Nicolas Chopin (NeurIPS, 2024)

Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds. - Pierre Alquier; William Kengne (CoRR, 2024)

Convergence of Statistical Estimators via Mutual Information Bounds. - E. L. Mahdi Khribch; Pierre Alquier (CoRR, 2024)

User-friendly Introduction to PAC-Bayes Bounds. - Pierre Alquier (Found. Trends Mach. Learn., 2024)