Pierre ALQUIER

Grade : Professeur à l'ENSAE


Bureau:
E01
Timbre:
J340
Lieu:
(MK1)
Labo:
LS

Téléphone : 0141175030

Mail : pierre.alquier[arrowbase]ensae.fr

ResearchEducationJobsBooksJournal articlesTeaching

Research Interests

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Aggregation of estimators. Model selection. High-dimensional statistics. Oracle & PAC-Bayesian inequalities. Applications.


Biography

Education

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2000-2003: ENSAE.

2002-2003: M2 "Probabilités et Applications" - Université Paris 6.

2003-2006: PhD in Statistics - Univ. Paris 6, advisor: Prof. O. Catoni.

2013: Habilitation in Statistics - Univ. Paris 6.


Jobs

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2006-2007: ATER (Teaching and Research Assistant) - Université Paris Dauphine.

2007-2012 : Maître de Conférences (Lecturer) - Université Paris 7.

2012-2014: Lecturer - University College Dublin.

2014-.... : Professeur de Statistique à l'ENSAE.



Publications

Books

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For a complete list of publications including preprints, and in order to download the papers, see my complete page: http://alquier.ensae.net/

As an editor

Alquier, P., Gautier, E. & Stoltz, G. (Editors), Inverse Problems and High-Dimensionnal Estimation, 2011, Stats in the Chateau Summer School, August 31 - September 4 2009, Springer - Lecture Notes in Statistics n. 203.


Journal articles

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Peer-reviewed conferences and journal papers:

Mai, T. T. & Alquier, Pseudo-Bayesian Quantum Tomography with Rank-adaptation, Journal of Statistical Planning and Inference, 2017, vol. 184, pp. 62-76.

Carel, L. & Alquier, P., Non-negative Matrix Factorization as a Pre-processing tool for Travelers Temporal Profiles Clustering, Proceedings of ESANN 2017, M. Verleysen Edt., i6doc.com Publ., pp. 417-422.

Alquier, P., Mai, T. T. & Pontil, M., Regret Bounds for Lifelong Learning, Proceedings ot AISTATS'17, PMLR, 2017, vol. 54, pp. 261-269.

Alquier, P. & Guedj, B., An Oracle Inequality for Quasi-Bayesian Non-negative Matrix Factorization, Mathematical Methods of Statistics, 2017, vol. 26, no. 1, pp. 55-67.

Alquier, P., Ridgway, J. and Chopin, N., On the Properties of Variational Approximations of Gibbs Posteriors, Journal of Machine Learning Research, 2016, vol. 17, no. 239, pp. 1-41.

Alquier, P., Friel, N., Everitt, R. G. & Boland, A, Noisy Monte-Carlo: Convergence of Markov Chains with Approximate Transition Kernels, Statistics and Computing, 2016, vol. 26, no. 1, pp. 29-47.

Mai, T. T. & Alquier, P., A Bayesian Approach for Matrix Completion: Optimal Rates under General Sampling Distribution, Electronic Journal of Statistics, 2015, vol. 9, pp. 823-841.

Ridgway, J., Alquier, P., Chopin, N. & F. Liang, PAC-Bayesian AUC Classification and Ranking, Proceedings of NIPS'14, 2014, vol. 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence and K. Q. Weinberger Eds., Curran Associates, Inc., pp. 658-666.

Alquier, P., Li, X. & Wintenberger, O., Prediction of time series by statistical learning: general losses and fast rates, Dependence Modeling, 2013, vol. 1, pp. 65-93.

Alquier, P., Bayesian methods for low-rank matrix estimation: short survey anf theoretical study, Proceedings of ALT'13, 2013, S. Jain, R. Munos, F. Stephan and T. Zeugmann Eds., Springer Lecture Notes in Artificial Intelligence n. 8139, pp. 309-323.

Alquier, P., Butucea, C., Hebiri, M., Meziani, K. & Morimae, T., Rank-penalized estimation of a quantum system, Physical Review A, 2013, vol. 88, no. 3, paper 032113.

Alquier, P., Meziani, K. & Peyré, G., Adaptive estimation of the density matrix in quantum homodyne tomography with noisy data, Inverse Problems, 2013, vol. 29, no. 7, paper 075017.

Alquier, P. & Biau, G., Sparse single-index model, Journal of Machine Learning Research, 2013, vol. 14, pp. 243-280.

Guedj, B. & Alquier, P., PAC-Bayesian estimation and prevision in sparse additive models, Electronic Journal of Statistics, 2013, vol. 7, pp. 264-291.

Alquier, P. & Li, X., Prediction of quantiles by statistical learning and application to GDP forecasting, Proceedings of DS'12, 2012, J.-G. Ganascia, P. Lenca and J.-M. Petit Eds., Springer Lecture Notes in Artificial Intelligence n. 7569, pp. 22-36.

Alquier, P. & Hebiri, M., Transductive versions of the LASSO and the Dantzig Selector, Journal of Statistical Planning and Inference, 2012, vol. 142, no. 9, pp. 2485-2500.

Alquier, P. & Wintenberger, O., Model selection for weakly dependent time series forecasting, Bernoulli, 2012, vol. 18, no. 13, pp. 883-913.

Alquier, P. & Hebiri, M., Generalization of L1 constraint for high dimensional regression problems, Statistics and Probability Letters, 2011, vol. 81, no. 12, pp . 1760-1765.

Alquier, P. & Doukhan, P., Sparsity considerations for dependent variables, Electronic Journal of Statistics, 2011, vol. 5, pp. 750-774.

Alquier, P. & Lounici, K., PAC-Bayesian Theorems for Sparse Regression Estimation with Exponential Weights, Electronic Journal of Statistics, 2011, vol. 5, pp. 127-145.

Alquier, P., An algorithm for iterative selection of blocks of features, Proceedings of ALT'10, 2010, M. Hutter, F. Stephan, V. Vovk and T. Zeugmann Eds., Springer - Lecture Notes in Artificial Intelligence n. 6331, pp. 35-49.

Alquier, P., PAC-Bayesian bounds for randomized empirical risk minimizers, Mathematical Methods of Statistics, 2008, vol.17, no. 4, pp. 279-304.

Alquier, P., LASSO, Iterative Feature Selection and the Correlation Selector: Oracle Inequalities and Numerical Performances, Electronic Journal of Statistics, 2008, vol.2, pp. 1129-1152.

Alquier, P., Density estimation with quadratic loss: a confidence intervals method, ESAIM P&S, 2008, vol. 12, pp. 438-463.

Alquier, P., Iterative feature selection in regression estimation, Annales de l'Institut Henri Poincaré: Probabilités et Statistiques, 2008, vol. 44, no. 1, pp. 47-88.

PhD and Habilitation Thesis:

Alquier, P., Contribution to Statistical Learning in Sparse Models, Habilitation Thesis, University Paris 6, 2013.

Alquier, P., Transductive and Inductive Adaptative Inference for Regression and Density Estimation, PhD Thesis, University Paris 6, 2006, funded by the CREST, advisor: Prof. Olivier Catoni (CNRS).



Teaching

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Calcul différentiel et intégral (1A ECO).

Introduction aux processus (2A).

Organisation du séminaire de modélisation statistique (2A).

Online learning & aggregation (3A ENSAE & M2 "Big Data" - Ecole Polytechnique, Telecom ParisTech).


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