activity
20182020
most citedScaled minimax optimality in high-dimensional linear regression: A non-convex algorithmic regularization approach

2 citations · 2 across the 1 of their papers we have counts for

collaborators

8 papers

math.ST2020

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…

math.ST20202 cited

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…

math.ST2019

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…

math.ST2018

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…

math.ST2018

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…

math.PR2018

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…