7 papers
Nonparametric Kernel Clustering with Bandit Feedback
Victor Thuot, Sebastian Vogt, Debarghya Ghoshdastidar +1
Clustering with bandit feedback refers to the problem of partitioning a set of items, where the clustering algorithm can sequentially query the items to receive noisy observations.…
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…
Phase Transition for Stochastic Block Model with more than Communities (II)
Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen
A fundamental theoretical question in network analysis is to determine under which conditions community recovery is possible in polynomial time in the Stochastic Block Model (SBM).…
Low-degree lower bounds via almost orthonormal bases
Alexandra Carpentier, Simone Maria Giancola, Christophe Giraud +1
Low-degree polynomials have emerged as a powerful paradigm for providing evidence of statistical-computational gaps across a variety of high-dimensional statistical models [Wein25]…
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…