ALQUIER Pierre

Professor (Full), ESSEC Business School

• 2013 : Habilitation à diriger des recherches (Université Pierre et Marie Curie (UPMC), France)
• 2013 : Habilitation à diriger des recherches (Université Pierre et Marie Curie (UPMC), France)
• 2006 : PhD (mathematical statistics) (Université Pierre et Marie Curie (UPMC), France)
• 2006 : PhD (mathematical statistics) (Université Pierre et Marie Curie (UPMC), France)
• 2003 : Diplôme de statisticien-économiste (L'École nationale de la statistique et de l'administration économique (ENSAE), France)
• 2003 : Diplôme de statisticien-économiste (L'École nationale de la statistique et de l'administration économique (ENSAE), France)
• 2003 : MSc in Probability Theory and Statistics (Université Pierre et Marie Curie (UPMC), France)
• 2003 : MSc in Probability Theory and Statistics (Université Pierre et Marie Curie (UPMC), France)

Full-time academic appointments

• 2023 - Present : Professor (ESSEC Business School, Singapore)
• 2023 - Present : Professor (ESSEC Business School, Singapore)

Other Academic Appointments

• 2019 - 2022 : Research scientist (RIKEN, Japan)
• 2019 - 2022 : Research scientist (RIKEN, Japan)
• 2014 - 2019 : Professor (L'École nationale de la statistique et de l'administration économique (ENSAE), France)
• 2014 - 2019 : Professor (L'École nationale de la statistique et de l'administration économique (ENSAE), France)
• 2012 - 2014 : Lecturer (UCD Dublin, Ireland)
• 2012 - 2014 : Lecturer (UCD Dublin, Ireland)
• 2007 - 2012 : Assistant Professor (Université Paris-Diderot (Paris VII), France)
• 2007 - 2012 : Assistant Professor (Université Paris-Diderot (Paris VII), France)
• 2006 - 2007 : Teaching and Research Assistant (Université Paris Dauphine, France)
• 2006 - 2007 : Teaching and Research Assistant (Université Paris Dauphine, France)

• 2019 : Best Paper Award (Asian Conference on Machine Learning, Japan)
• 2019 : Best Paper Award (Asian Conference on Machine Learning, Japan)

• ALQUIER, P. and GERBER, M. (2023). Universal Robust Regression via Maximum Mean Discrepancy. Biometrika, In press.
• FAN, X., ALQUIER, P. and DOUKHAN, P. (2022). Deviation inequalities for stochastic approximation by averaging. Stochastic Processes and their Applications, 152, pp. 452-485.
• ALQUIER, P., MARIE, N. and ROSIER, A. (2022). Tight risk bound for high dimensional time series completion. The Electronic Journal of Statistics, 16(1), pp. 3001-3035.
• CHERIEF-ABDELLATIF, B.E. and ALQUIER, P. (2022). Finite sample properties of parametric MMD estimation: Robustness to misspecification and dependence. Bernoulli: A Journal of Mathematical Statistics and Probability, 28(1), pp. 181-213.
• ALQUIER, P., CHERIEF-ABDELLATIF, B.E., DERUMIGNY, A. and FERMANIAN, J.D. (2022). Estimation of Copulas via Maximum Mean Discrepancy. Journal of the American Statistical Association, In press.
• MEUNIER, D. and ALQUIER, P. (2021). Meta-Strategy for Learning Tuning Parameters with Guarantees. Entropy, 23(10).
• CAREL, L. and ALQUIER, P. (2021). Simultaneous dimension reduction and clustering via the NMF-EM algorithm. Advances in Data Analysis and Classification, 15(1), pp. 231-260.
• ALQUIER, P. and RIDGWAY, J. (2020). Concentration of tempered posteriors and of their variational approximations. Annals of Statistics, 48(3), pp. 1475-1497.
• ALQUIER, P., BERTIN, K., DOUKHAN, P. and GARNIER, R. (2020). High-dimensional VAR with low-rank transition. Statistics and Computing, 30(4), pp. 1139-1153.
• ALQUIER, P., COTTET, V. and LECUE, G. (2019). Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions. Annals of Statistics, 47(4), pp. 2117-2144.
• ALQUIER, P. and MARIE, N. (2019). Matrix factorization for multivariate time series analysis. The Electronic Journal of Statistics, 13(2), pp. 4346-4366.
• ALQUIER, P., DOUKHAN, P. and FAN, X. (2019). Exponential inequalities for nonstationary Markov chains. Dependence Modeling, 7(1), pp. 150-168.
• MAIRE, F., FRIEL, N. and ALQUIER, P. (2019). Informed sub-sampling MCMC: approximate Bayesian inference for large datasets. Statistics and Computing, 29(3), pp. 449-482.
• CHERIEF-ABDELLATIF, B.E. and ALQUIER, P. (2018). Consistency of variational Bayes inference for estimation and model selection in mixtures. The Electronic Journal of Statistics, 12(2), pp. 2995-3035.
• ALQUIER, P. and GUEDJ, B. (2018). Simpler PAC-Bayesian bounds for hostile data. Machine Learning, 107(5), pp. 887-902.
• COTTET, V. and ALQUIER, P. (2018). 1-Bit matrix completion: PAC-Bayesian analysis of a variational approximation. Machine Learning, 107(3), pp. 579-603.
• MAI, T.T. and ALQUIER, P. (2017). Pseudo-Bayesian quantum tomography with rank-adaptation. Journal of Statistical Planning and Inference, 184, pp. 62-76.
• ALQUIER, P. and GUEDJ, B. (2017). An oracle inequality for quasi-Bayesian nonnegative matrix factorization. Mathematical Methods of Statistics, 26(1), pp. 55-67.
• ALQUIER, P., RIDGWAY, J. and CHOPIN, N. (2016). On the Properties of Variational Approximations of Gibbs Posteriors. Journal of Machine Learning Research, 17(239), pp. 1-41.
• ALQUIER, P., FRIEL, N., EVERITT, R. and BOLAND, A. (2016). Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels. Statistics and Computing, 26(1-2), pp. 29-47.
• MAI, T.T. and ALQUIER, P. (2015). A Bayesian approach for noisy matrix completion: Optimal rate under general sampling distribution. The Electronic Journal of Statistics, 9(1), pp. 823-841.
• ALQUIER, P., LI, X. and WINTENBERGER, O. (2013). Prediction of time series by statistical learning: general losses and fast rates. Dependence Modeling, 1, pp. 65-93.
• ALQUIER, P., BUTUCEA, C., HEBIRI, M., MEZIANI, K. and MORIMAE, T. (2013). Rank-penalized estimation of a quantum system. Physical Review A, 88(3).
• ALQUIER, P., MEZIANI, K. and PEYRÉ, G. (2013). Adaptive estimation of the density matrix in quantum homodyne tomography with noisy data. Inverse Problems, 29(7), pp. 075017.
• ALQUIER, P. and BIAU, G. (2013). Sparse Single-Index Model. Journal of Machine Learning Research, 14, pp. 243-280.
• GUEDJ, B. and ALQUIER, P. (2013). PAC-Bayesian estimation and prediction in sparse additive models. The Electronic Journal of Statistics, 7, pp. 264-291.
• ALQUIER, P. and WINTENBERGER, O. (2012). Model selection for weakly dependent time series forecasting. Bernoulli: A Journal of Mathematical Statistics and Probability, 18(3), pp. 883-913.
• ALQUIER, P. and HEBIRI, M. (2012). Transductive versions of the LASSO and the Dantzig Selector. Journal of Statistical Planning and Inference, 142(9), pp. 2485-2500.
• ALQUIER, P. and HEBIRI, M. (2011). Generalization of constraints for high dimensional regression problems. Statistics & Probability Letters, 81(12), pp. 1760-1765.
• ALQUIER, P. and DOUKHAN, P. (2011). Sparsity considerations for dependent variables. The Electronic Journal of Statistics, 5, pp. 750-774.
• ALQUIER, P. and LOUNICI, K. (2011). PAC-Bayesian bounds for sparse regression estimation with exponential weights. The Electronic Journal of Statistics, 5, pp. 127-145.
• ALQUIER, P. (2008). PAC-Bayesian bounds for randomized empirical risk minimizers. Mathematical Methods of Statistics, 17(4), pp. 279-304.
• ALQUIER, P. (2008). LASSO, Iterative Feature Selection and the Correlation Selector: Oracle inequalities and numerical performances. The Electronic Journal of Statistics, 2, pp. 1129-1152.
• ALQUIER, P. (2008). Density estimation with quadratic loss: a confidence intervals method. ESAIM: Probability and Statistics, 12, pp. 438-463.
• ALQUIER, P. (2008). Iterative feature selection in least square regression estimation. Annales de l Institut Henri Poincare-Probabilites et Statistiques, 44(1), pp. 47-88.

• ALQUIER, P. [Ed] (2022). Approximate Bayesian Inference. MDPI.
• ALQUIER, P., GAUTIER, E. and STOLTZ, G. [Eds] (2011). Inverse Problems and High-Dimensional Estimation. Springer Berlin Heidelberg.

• ALQUIER, P. (2013). Constributions to Statistical Learning in Sparse Models. Paris: France.

• SAKHI, O., ALQUIER, P. and CHOPIN, N. (2023). PAC-Bayesian Offline Contextual Bandits With Guarantees. In: 40th International Conference on Machine Learning (ICML). Hawaii: Proceedings of Machine Learning Research, pp. 29777-29799.
• MAI, T.T. and ALQUIER, P. (2022). Understanding the Population Structure Correction Regression. In: 4th International Conference on Statistics: Theory and Applications (ICSTA'22). Prague: Avestia Publishing.
• ALQUIER, P. (2021). Non-exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions. In: 38th International Conference on Machine Learning (ICML'21). Proceedings of Machine Learning Research.
• DOAN, T., ABBANA BENNANI, M., MAZOURE, B., RABUSSEAU, G. and ALQUIER, P. (2021). A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. In: 24th International Conference on Artificial Intelligence and Statistics (AIStat'21). Proceedings of Machine Learning Research.
• CHERIEF-ABDELLATIF, B.E. and ALQUIER, P. (2020). MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy. In: 2nd Symposium on Advances in Approximate Bayesian Inference (AABI'19). Proceedings of Machine Learning Research.
• CHERIEF-ABDELLATIF, B.E., ALQUIER, P. and KHAN, M.E. (2019). A Generalization Bound for Online Variational Inference. In: 11th Asian Conference on Machine Learning (ACML'19). Proceedings of Machine Learning Research.
• ALQUIER, P., MAI, T.T. and PONTIL, M. (2017). Regret Bounds for Lifelong Learning. In: 20th International Conference on Artificial Intelligence and Statistics (AIStat'17). Proceedings of Machine Learning Research.
• CAREL, L. and ALQUIER, P. (2017). Non-negative Matrix Factorization as a Pre-processing tool for Travelers Temporal Profiles Clustering. In: 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'17). i6doc.com.
• RIDGWAY, J., ALQUIER, P., CHOPIN, N. and LIANG, F. (2014). PAC-Bayesian AUC Classification and Scoring. In: 28th Conference on Neural Information Processing Systems (NIPS'14). Curran Associates, Inc.
• ALQUIER, P. (2013). Bayesian Methods for Low-Rank Matrix Estimation: Short Survey and Theoretical Study. In: 24th International Conference on Algorithmic Learning Theory (ALT'13). Singapore: Springer Berlin Heidelberg, pp. 309-323.
• ALQUIER, P. and LI, X. (2012). Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting. In: 15th International Conference on Discovery Science (DS'12). Lyon: Springer Berlin Heidelberg, pp. 22-36.
• ALQUIER, P. (2010). An Algorithm for Iterative Selection of Blocks of Features. In: 21st International Conference on Algorithmic Learning Theory (ALT'10). Caberra: Springer Berlin Heidelberg, pp. 35-49.

• ALQUIER, P. (2020). Approximate Bayesian Inference. Entropy, 22(11), pp. 1272.

• ALQUIER, P. 2023. ChatGPT. March.
• ALQUIER, P. 2023. Generative AI: Friend or Foe? March.