6 citations · 12 across the 11 of their papers we have counts for
18 papers
Sub-Gaussian Confidence Intervals for Heavy-Tailed Data: Characterizing the Limits of Inference
Ilyes Hammouda, Stanislav Minsker, Mohamed Ndaoud
We investigate the problem of uncertainty quantification in the mean estimation framework. Given an i.i.d. sample from a distribution with unknown mean and variance , we w…
Sharp and Adaptive Cluster Recovery in Slowly Mixing Gaussian Hidden Markov Models
Ibrahim Kaddouri, Mohamed Ndaoud
We study cluster recovery in a two-state Gaussian hidden Markov model in the slowly mixing regime, where the transition probability may vanish with the sample size. Temporal pe…
Robust Regression under Adversarial Contamination: Theory and Algorithms for the Welsch Estimator
Ilyes Hammouda, Mohamed Ndaoud, Abd-Krim Seghouane
Convex and penalized robust regression methods often suffer from a persistent bias induced by large outliers, limiting their effectiveness in adversarial or heavy-tailed settings.…
Ask for More Than Bayes Optimal: A Theory of Indecisions for Selective Hypothesis Testing
Mohamed Ndaoud, Peter Radchenko, Bradley Rava
Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confident and abstain when uncertainty is hi…
Adaptive Mean Estimation in the Hidden Markov sub-Gaussian Mixture Model
Vahe Karagulyan, Mohamed Ndaoud
We investigate the problem of center estimation in the high dimensional binary sub-Gaussian Mixture Model with Hidden Markov structure on the labels. We first study the limitations…
Robust and Tuning-Free Sparse Linear Regression via Square-Root Slope
Stanislav Minsker, Mohamed Ndaoud, Lang Wang
We consider the high-dimensional linear regression model and assume that a fraction of the measurements are altered by an adversary with complete knowledge of the data and the unde…
Variable selection, monotone likelihood ratio and group sparsity
Cristina Butucea, Enno Mammen, Mohamed Ndaoud +1
In the pivotal variable selection problem, we derive the exact non-asymptotic minimax selector over the class of all -sparse vectors, which is also the Bayes selector with respe…
Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model: A new take on Robust Interpolation
Stanislav Minsker, Mohamed Ndaoud, Yiqiu Shen
We study the supervised clustering problem under the two-component anisotropic Gaussian mixture model in high dimensions and in the non-asymptotic setting. We first derive a lower…
Near-Optimal Procedures for Model Discrimination with Non-Disclosure Properties
Dmitrii M. Ostrovskii, Mohamed Ndaoud, Adel Javanmard +1
Let be the population risk minimizers associated to some loss and two distributions $\mathbb{P}_0,\mat…
Scaled minimax optimality in high-dimensional linear regression: A non-convex algorithmic regularization approach
Mohamed Ndaoud
The question of fast convergence in the classical problem of high dimensional linear regression has been extensively studied. Arguably, one of the fastest procedures in practice is…
Robust and efficient mean estimation: an approach based on the properties of self-normalized sums
Stanislav Minsker, Mohamed Ndaoud
Let be a random variable with unknown mean and finite variance. We present a new estimator of the mean of that is robust with respect to the possible presence of outliers i…
Improved clustering algorithms for the Bipartite Stochastic Block Model
Mohamed Ndaoud, Suzanne Sigalla, Alexandre B. Tsybakov
We establish sufficient conditions of exact and almost full recovery of the node partition in Bipartite Stochastic Block Model (BSBM) using polynomial time algorithms. First, we im…
Sharp optimal recovery in the two-component Gaussian Mixture Model
Mohamed Ndaoud
This paper studies the problem of clustering in the two-component Gaussian mixture model where the centers are separated by for some . We characterize the exact phase tra…
Interplay of minimax estimation and minimax support recovery under sparsity
Mohamed Ndaoud
In this paper, we study a new notion of scaled minimaxity for sparse estimation in high-dimensional linear regression model. We present more optimistic lower bounds than the one gi…
Harmonic analysis meets stationarity: A general framework for series expansions of special Gaussian processes
M. Ndaoud
In this paper, we present a new approach to derive series expansions for some Gaussian processes based on harmonic analysis of their covariance function. In particular, we propose…
Optimal variable selection and adaptive noisy Compressed Sensing
Mohamed Ndaoud, Alexandre B. Tsybakov
In the context of high-dimensional linear regression models, we propose an algorithm of exact support recovery in the setting of noisy compressed sensing where all entries of the d…
Adaptive robust estimation in sparse vector model
Laëtitia Comminges, Olivier Collier, Mohamed Ndaoud +1
For the sparse vector model, we consider estimation of the target vector, of its L2-norm and of the noise variance. We construct adaptive estimators and establish the optimal rates…
Variable selection with Hamming loss
Cristina Butucea, Mohamed Ndaoud, Natalia A. Stepanova +1
We derive non-asymptotic bounds for the minimax risk of variable selection under expected Hamming loss in the Gaussian mean model in for classes of -sparse vector…