## Pierre ALQUIER

Professor

department: Information Systems, Decision Sciences and Statistics

Campus de Singapour

Statistical Data Analysis – Probability Theory & Mathematical Statistics

**Personal links**

**Biography**

Professor (Full), ESSEC Business School

Diplomas

- 2013: Habilitation à diriger des recherches (Université Pierre et Marie Curie (UPMC) France)
- 2006: PhD (mathematical statistics) (Université Pierre et Marie Curie (UPMC) France)
- 2003: MSc in Probability Theory and 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)

**Career**

**Full-time academic appointments**

- 2023 – Now : Professor (ESSEC Business School Singapore)

**Other appointments**

- 2019 – 2022 : Research scientist (RIKEN Japan)

**Other Academic Appointments**

- 2014 – 2019 : Professor (L’École nationale de la statistique et de l’administration économique (ENSAE) France)
- 2012 – 2014 : Lecturer (UCD Dublin Ireland)
- 2007 – 2012 : Assistant Professor (Université Paris Diderot (Paris VII) France)
- 2006 – 2007 : Teaching and Research Assistant (Université Paris Dauphine France)

#### Awards

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

### Presentations at an Academic or Professional conference

- ALQUIER, P., RIOU, C. et CHÉRIEF-ABDELLATIF, B.E. (2024). Rates of Convergence in Bayesian Meta-learning. Dans: 2024 IMS Asia-Pacific Rim Meeting. Melbourne.
- ALQUIER, P., RIOU, C. et CHÉRIEF-ABDELLATIF, B.E. (2023). Rates of convergence in Bayesian meta-learning. Dans: 6th International Conference on Econometrics and Statistics 2023. Tokyo.
- ALQUIER, P. et CHÉRIEF-ABDELLATIF, B.E. (2023). Fast Rates in Meta-Learning with PAC-Bayes Bounds. Dans: 12th Workshop on High Dimensional Data Analysis 2023. Rabat.

### Interviews: radio, TV, press

### Conference Proceedings

- SAKHI, O., ALQUIER, P. et CHOPIN, N. (2023). PAC-Bayesian Offline Contextual Bandits With Guarantees. Dans:
*40th International Conference on Machine Learning (ICML)*. Hawaii: Proceedings of Machine Learning Research, pp. 29777-29799. - MAI, T.T. et ALQUIER, P. (2022). Understanding the Population Structure Correction Regression. Dans:
*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. Dans:
*38th International Conference on Machine Learning (ICML’21)*. Proceedings of Machine Learning Research. - DOAN, T., ABBANA BENNANI, M., MAZOURE, B., RABUSSEAU, G. et ALQUIER, P. (2021). A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix. Dans:
*24th International Conference on Artificial Intelligence and Statistics (AIStat’21)*. Proceedings of Machine Learning Research. - CHERIEF-ABDELLATIF, B.E. et ALQUIER, P. (2020). MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy. Dans:
*2nd Symposium on Advances in Approximate Bayesian Inference (AABI’19)*. Proceedings of Machine Learning Research. - CHERIEF-ABDELLATIF, B.E., ALQUIER, P. et KHAN, M.E. (2019). A Generalization Bound for Online Variational Inference. Dans:
*11th Asian Conference on Machine Learning (ACML’19)*. Proceedings of Machine Learning Research. - CAREL, L. et ALQUIER, P. (2017). Non-negative Matrix Factorization as a Pre-processing tool for Travelers Temporal Profiles Clustering. Dans:
*25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN’17)*. i6doc.com. - ALQUIER, P., MAI, T.T. et PONTIL, M. (2017). Regret Bounds for Lifelong Learning. Dans:
*20th International Conference on Artificial Intelligence and Statistics (AIStat’17)*. Proceedings of Machine Learning Research. - RIDGWAY, J., ALQUIER, P., CHOPIN, N. et LIANG, F. (2014). PAC-Bayesian AUC Classification and Scoring. Dans:
*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. Dans:
*24th International Conference on Algorithmic Learning Theory (ALT’13)*. Singapore: Springer Berlin Heidelberg, pp. 309-323. - ALQUIER, P. et LI, X. (2012). Prediction of Quantiles by Statistical Learning and Application to GDP Forecasting. Dans:
*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. Dans:
*21st International Conference on Algorithmic Learning Theory (ALT’10)*. Caberra: Springer Berlin Heidelberg, pp. 35-49.

### Journal articles

- ALQUIER, P., CHERIEF-ABDELLATIF, B.E., DERUMIGNY, A. et FERMANIAN, J.D. (2023). Estimation of Copulas via Maximum Mean Discrepancy.
*Journal of the American Statistical Association*, 118(543), pp. 1997-2012. - ALQUIER, P. et GERBER, M. (2023). Universal Robust Regression via Maximum Mean Discrepancy.
*Biometrika*, In press. - FAN, X., ALQUIER, P. et DOUKHAN, P. (2022). Deviation inequalities for stochastic approximation by averaging.
*Stochastic Processes and their Applications*, 152, pp. 452-485. - CHERIEF-ABDELLATIF, B.E. et 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., MARIE, N. et ROSIER, A. (2022). Tight risk bound for high dimensional time series completion.
*The Electronic Journal of Statistics*, 16(1), pp. 3001-3035. - MEUNIER, D. et ALQUIER, P. (2021). Meta-Strategy for Learning Tuning Parameters with Guarantees.
*Entropy*, 23(10). - CAREL, L. et 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., BERTIN, K., DOUKHAN, P. et GARNIER, R. (2020). High-dimensional VAR with low-rank transition.
*Statistics and Computing*, 30(4), pp. 1139-1153. - ALQUIER, P. et RIDGWAY, J. (2020). Concentration of tempered posteriors and of their variational approximations.
*Annals of Statistics*, 48(3), pp. 1475-1497. - ALQUIER, P. et MARIE, N. (2019). Matrix factorization for multivariate time series analysis.
*The Electronic Journal of Statistics*, 13(2), pp. 4346-4366. - ALQUIER, P., COTTET, V. et 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., DOUKHAN, P. et FAN, X. (2019). Exponential inequalities for nonstationary Markov chains.
*Dependence Modeling*, 7(1), pp. 150-168. - MAIRE, F., FRIEL, N. et 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. et 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. et GUEDJ, B. (2018). Simpler PAC-Bayesian bounds for hostile data.
*Machine Learning*, 107(5), pp. 887-902. - COTTET, V. et ALQUIER, P. (2018). 1-Bit matrix completion: PAC-Bayesian analysis of a variational approximation.
*Machine Learning*, 107(3), pp. 579-603. - MAI, T.T. et ALQUIER, P. (2017). Pseudo-Bayesian quantum tomography with rank-adaptation.
*Journal of Statistical Planning and Inference*, 184, pp. 62-76. - ALQUIER, P. et 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. et 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. et 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. et 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. et 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. et MORIMAE, T. (2013). Rank-penalized estimation of a quantum system.
*Physical Review A*, 88(3). - ALQUIER, P., MEZIANI, K. et PEYRÉ, G. (2013). Adaptive estimation of the density matrix in quantum homodyne tomography with noisy data.
*Inverse Problems*, 29(7), pp. 075017. - ALQUIER, P. et BIAU, G. (2013). Sparse Single-Index Model.
*Journal of Machine Learning Research*, 14, pp. 243-280. - GUEDJ, B. et ALQUIER, P. (2013). PAC-Bayesian estimation and prediction in sparse additive models.
*The Electronic Journal of Statistics*, 7, pp. 264-291. - ALQUIER, P. et 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. et 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. et HEBIRI, M. (2011). Generalization of constraints for high dimensional regression problems.
*Statistics & Probability Letters*, 81(12), pp. 1760-1765. - ALQUIER, P. et DOUKHAN, P. (2011). Sparsity considerations for dependent variables.
*The Electronic Journal of Statistics*, 5, pp. 750-774. - ALQUIER, P. et 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.

### Book editor

### Prefaces of a journal

**Activités de recherche**

- 2022 – Now: Action Editor: Transactions in Machine Learning Research
- 2020 – Now: Action Editor: Journal of Machine Learning Research
- 2020 – 2022: Topic Advisory Panel: Entropy

**Theses**