Publications

• Hammouda, I., Ndaoud, M., Seghouane, K., Outlier-Bias Removal with Alpha Divergence: A Robust Non-Convex Estimator for Linear Regression (2024) preprint.

• Ndaoud, M., Radchenko, P., Rava, B. Ask for More Than Bayes Optimal: A Theory of Indecisions for Classification (2024) preprint.

• Karagulyan, V., Ndaoud, M. Adaptive Mean Estimation in the Hidden Markov sub-Gaussian Mixture Model. (2024) submitted.

• Minsker, S., Ndaoud, M.,Shen, Y. Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model:  A new take on Robust Interpolation. (2023) Major revision.

• Ndaoud, M. Fast and adaptive iterative hard thresholding in high dimensional linear regression: A non-convex algorithmic regularization approach. (2022) Submitted

• Ndaoud, M., Ostrovskii, D., Javanmard, A., Razaviyayn, M. Near-Optimal Model Discrimination With Non-Disclosure. (2022) Major revision. 

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• Minsker, S., Ndaoud, M., Lang, W. Robust and Tuning-Free Sparse Linear Regression via Square-Root Slope. (2023) SIAM Journal on Mathematics of Data Science.

• Butucea, C., Mammen, E., Ndaoud, M., Tsybakov, A.B. Variable selection, monotone likelihood ratio and group sparsity. (2023) Annals of Statistics.

• Ndaoud, M. Harmonic analysis meets stationarity: A general framework for series expansions of special Gaussian processes. (2023) Bernoulli.

• Ndaoud, M., Sigalla, S., Tsybakov, A.B. (2022). Improved clustering algorithms for the Bipartite Stochastic Block Model. IEEE Information Theory.

• Ndaoud, M. Sharp optimal recovery in the two Gaussian Mixture Model. (2022) Annals of Statistics.

• Minsker, S., Ndaoud, M. Robust and efficient mean estimation: approach based on the properties of self-normalized sums. (2021) Electronic Journal of Statistics. 

• Comminges, L., Collier, O., Ndaoud, M., & Tsybakov, A.B.  Adaptive robust estimation in sparse vector model. (2021) Annals of Statistics.

• Ndaoud, M., Tsybakov, A. B. Optimal variable selection and adaptive noisy Compressed Sensing. (2020) IEEE Information Theory.

• Ndaoud, M. Interplay of minimax estimation and minimax support recovery under sparsity. (2018) Algorithmic Learning Theory (ALT) 2019 [Best Student Paper Award]

• Butucea, C., Ndaoud, M., Stepanova, N. A., & Tsybakov, A. B. (2018). Variable selection with Hamming loss. Annals of Statistics, 46(5), 1837-1875.

Seminar talks and conferences

• ISNPS 2024, a talk about "Adaptive Mean Estimation in the Hidden Markov sub-Gaussian Mixture Model", 25-29th June 2024, Braga, Portugal.

• Journées MAS 2024, organising a session on "high dimensional statistics", 28-30th August 2024, Poitiers, France.

• Séminaire Parisien de statistique, talk about "On some recent advances in high dimensional binary sub-Gaussian mixture models", June 10th 2024, Paris, France.

• IMS Asia Pacific Rim Meeting (IMS-APRM), talk about "Robust and Tuning-Free Sparse Linear regression via Square-Root Slope", 4-7th January 2024, Melbourne, Australia.

• Mathematics & Decision Conference, talk about "mathematical foundations of robustness", 11-15th December 2023, Ben Guerir, Morocco.

• McGill Statistics Seminar, talk about "Robust and Tuning-Free Sparse Linear regression via Square-Root Slope", November 17th 2023, online.

• Orsay Probability and Statistics Seminar, talk about "Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model: A new take on Robust Interpolation", October 5th 2023, Orsay, France.

• AUB math seminar, talk about "Robust and efficient mean estimation: an approach based on the properties of self-normalized sums", May 26th 2023, Beirut, Lebanon.

• IMS ICSDS 2022, talk about "Adaptive robustness and sub-Gaussian deviations in sparse linear regression", December 13th-16th, Florence 2022, Italy.

• TSE statistics and math seminar, talk about "Adaptive robustness and sub-Gaussian deviations in sparse linear regression", October 6th 2022, Toulouse , France.

• USC probability and statistics seminar,  talk about "Adaptive robustness and sub-Gaussian deviations in sparse linear regression", September 30th 2022, Los Angeles, USA.

• ISNPS 2022, organising a session about "New trends in high dimensional robust statistics", June 20th-24th 2022, Paphos, Cyprus. 

• IMS Annual Meeting 2022,  talk about "Variable selection, monotone likelihood ratio and group sparsity"June 27th-30th 2022, London, UK.

• Re-thinking High Dimensional Statistics 2022, talk about "Adaptive robustness and sub-Gaussian deviations in sparse linear regression", Oberwolfach, Germany, May 15th-21st, 2022

• CREST Statistics Seminar, talk about "Variable selection, monotone likelihood ratio and group sparsity", Palaiseau, France, April 4th, 2022

• Meeting in Mathematical Statistics 2021, talk about "Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model: A new take on Robust Interpolation", CIRM Marseille, France, December 12th-16th, 2021

• Rutgers Statistics Seminar, talk about "Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model: A new take on Robust Interpolation", New Jersey, November 3rd, 2021

• Mathematical Statistics and Learning 2021, talk about "Interpolation vs Regularization in high dimensional mixture models", Barcelona, June 29th-July 2nd, 2021

• Stanford Statistics Seminar, Autumn 2020, talk about "Algorithmic regularization in High dimensional linear regression" September 29th, 2020 (Online)

• Bernoulli-IMS One World Symposium 2020, talk about "Algorithmic regularization in High dimensional linear regression" August 24th-August 28th, 2020 (Online)

• Joint Statistical meetings 2020, talk about "Algorithmic regularization in High dimensional linear regression", Pennsylvania Convention Center, August 1st-August 6th, 2020 (Online)

• Joint Statistical meetings 2020, talk about "Improved clustering  for the Bipartite Stochastic Block Model", Pennsylvania Convention Center, August 1st-August 6th, 2020 (Online)

• Young Data Science Researcher Seminar, talk about "Algorithmic regularization in High dimensional linear regression", Zurich,  June 12th, 2020 (Online)

• Meeting in Mathematical Statistics 2019, CIRM Marseille, France, December 16th-20th, 2019

• The 2019 Southern California Probability Symposium, talk about "Improved clustering for the Bipartite Stochastic Block Model",  the IPAM at UCLA, Los Angeles, December 7th, 2019

• Probability and Statistics Seminar, talk about "Algorithmic regularization in High dimensional linear regression", Department of Mathematics at USC, Los Angeles, October 11th, 2019

• ISI WSC 2019, talk about "Adaptive robust estimation in sparse vector model", Kuala Lumpur Convention Center, August 18th-August 23rd, 2019

• Workshop "Optimization and Statistical Learning", Les Houches, France, March 24th-29th, 2019

• ALT 2019, Chicago, March 22-24th, 2019 [Best Student Paper Award]

• CREST seminar, talk about "Algorithmic regularization in High dimensional linear regression", Paris, January 28th, 2019

• Meeting in Mathematical Statistics 2018, talk about "Exact recovery in the Two Gaussian Mixture Model". Frejus, France, December 16th-21th, 2018

• Talk "Interplay of minimax estimation and minimax support recovery under sparsity", Heidelberg, November 22nd, 2018

• Workshop "Statistical Inference for Structured High-dimensional Models", Oberwolfach, March 11th-17th ,2018

• Workshop "Meeting in Mathematical Statistics", CIRM, France, December 18th-22th, 2017

• Joint Statistical meetings 2017, talk about "Optimal variable selection and noisy adaptive compressed sensing", Baltimore Convention Center, July 29th-August 3rd, 2017

• Summer School on "Spectral properties of large random objects", IHES Paris, July 15th-28th, 2017

• 47th Probability Summer School,  Saint-Flour, France, July 2nd-14th, 2017

• Les probabilités de demain 2017, talk about "Constructing the Fractional Brownian Motion".

• Workshop "Optimization and Statistical Learning", Les Houches, France, April 10th-14th, 2017

• Research School "ALÉA days", CIRM, France, March 20th-24th, 2017

• Spring School, Structural inference and statistics, Brodten, Germany, March 14th-18th, 2016

• BBQ Seminar, Lightening talk about  "Constructing the Fractional Brownian Motion", 2015

Grants

• 2021-2025: Chaire d'excellence junior en Data Science.

• 2020 - 2021 : Zumberge Individual Award 2020. (USC James H. Zumberge Faculty Research and Innovation Fund)

• 2020 - 2021 : IMS New Researcher Travel Award.

• 2016 - 2019 : AMX (Allocation-Moniteur Polytechnique) grant

• 2012 - 2016 : French Government's Major-Excellence Scholarship

Professional service

• Reviewer for Mathematical Statistics and Learning (MSL) : 2019 - 

• Reviewer for the Journal of the Royal Statistical Society: Series B (JRSS B) : 2020 - 

• Reviewer for IEEE Information Theory : 2019 - 

• Reviewer for JMLR : 2019 - 

• Reviewer for Statistica Sinica : 2019 - 

• Reviewer for Information and Inference : 2019 - 

• Reviewer for the Annals of Statistics (AoS) : 2018 - 

• Reviewer for Bernoulli : 2018 - 

• Reviewer for Neurips : 2019, 2020.

• Reviewer for ICML :  2020.