Pierre Alquier
Pierre Alquier
Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo
Verified email at postman.riken.jp - Homepage
Cited by
Cited by
Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels
P Alquier, N Friel, R Everitt, A Boland
Statistics and Computing 26 (1), 29-47, 2016
On the properties of variational approximations of Gibbs posteriors
P Alquier, J Ridgway, N Chopin
Journal of Machine Learning Research 17 (239), 1-41, 2016
Sparse single-index model
P Alquier, G Biau
Journal of Machine Learning Research 14 (Jan), 243-280, 2013
PAC-Bayesian bounds for sparse regression estimation with exponential weights
P Alquier, K Lounici
Electronic Journal of Statistics 5, 127-145, 2011
Model selection for weakly dependent time series forecasting
P Alquier, O Wintenberger
Bernoulli 18 (3), 883-913, 2012
PAC-Bayesian bounds for randomized empirical risk minimizers
P Alquier
Mathematical Methods of Statistics 17 (4), 279-304, 2008
PAC-Bayesian Estimation and Prediction in Sparse Additive Models
B Guedj, P Alquier
Electronic Journal of Statistics 7, 264-291, 2013
Concentration of tempered posteriors and of their variational approximations
P Alquier, J Ridgway
The Annals of Statistics 48 (3), 1475-1497, 2020
Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions
P Alquier, V Cottet, G Lecué
The Annals of Statistics 47 (4), 2117-2144, 2019
Regret bounds for lifelong learning
P Alquier, TT Mai, M Pontil
20th International Conference on Artificial Intelligence and Statistics, 261-269, 2017
Consistency of Variational Bayes Inference for Estimation and Model Selection in Mixtures
BE Chérief-Abdellatif, P Alquier
Electronic Journal of Statistics 12 (2), 2995-3035, 2018
A Bayesian Approach for Noisy Matrix Completion: Optimal Rate under General Sampling Distribution
TT Mai, P Alquier
Electronic Journal of Statistics 9, 823-841, 2015
Prediction of time series by statistical learning: general losses and fast rates
P Alquier, X Li, O Wintenberger
Dependence Modeling 1, 65-93, 2013
Simpler PAC-Bayesian bounds for hostile data
P Alquier, B Guedj
Machine Learning 107 (5), 887–902, 2018
Inverse problems and high-dimensional estimation: stats in the Château Summer School, August 31-September 4, 2009
P Alquier, E Gautier, G Stoltz
Springer Science & Business Media, 2011
Bayesian methods for low-rank matrix estimation: short survey and theoretical study
P Alquier
24th International Conference on Algorithmic Learning Theory, 309-323, 2013
Lasso, iterative feature selection and the correlation selector: Oracle inequalities and numerical performances
P Alquier
Electronic Journal of Statistics 2, 1129-1152, 2008
Rank penalized estimation of a quantum system
P Alquier, C Butucea, M Hebiri, K Meziani, T Morimae
Physical Review A 88 (3), 2013
1-Bit matrix completion: PAC-Bayesian analysis of a variational approximation
V Cottet, P Alquier
Machine Learning 107 (3), 579-603, 2018
Prediction of quantiles by statistical learning and application to GDP forecasting
P Alquier, X Li
15th International Conference on Discovery Science, 23-36, 2012
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