6 papers · 1 filter
Low-degree Lower bounds for clustering in moderate dimension
Alexandra Carpentier, Nicolas Verzelen
We study the fundamental problem of clustering points into groups drawn from a mixture of isotropic Gaussians in . Specifically, we investigate the requisite…
Statistical and computational challenges in ranking
Alexandra Carpentier, Nicolas Verzelen
We consider the problem of ranking experts according to their abilities, based on the correctness of their answers to questions. This is modeled by the so-called crowd-sour…
Minimax optimal seriation in polynomial time
Yann Issartel, Christophe Giraud, Nicolas Verzelen
We consider the seriation problem, whose goal is to recover a hidden ordering from a noisy observation of a permuted Robinson matrix. We establish sharp minimax rates under average…
Seriation of Toeplitz and latent position matrices: optimal rates and computational trade-offs
Clément Berenfeld, Alexandra Carpentier, Nicolas Verzelen
In this paper, we consider the problem of seriation of a permuted structured matrix based on noisy observations. The entries of the matrix relate to an expected quantification of i…
Computational barriers for permutation-based problems, and cumulants of weakly dependent random variables
Bertrand Even, Christophe Giraud, Nicolas Verzelen
In many high-dimensional problems,polynomial-time algorithms fall short of achieving the statistical limits attainable without computational constraints. A powerful approach to pro…
Computational lower bounds in latent models: clustering, sparse-clustering, biclustering
Bertrand Even, Christophe Giraud, Nicolas Verzelen
In many high-dimensional problems, like sparse-PCA, planted clique, or clustering, the best known algorithms with polynomial time complexity fail to reach the statistical performan…