activity
20102018
most citedThe Normalized Graph Cut and Cheeger Constant: from Discrete to Continuous

15 citations · 45 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2018★ 12 cited

Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning

Ery Arias-Castro, Adel Javanmard, Bruno Pelletier

One of the common tasks in unsupervised learning is dimensionality reduction, where the goal is to find meaningful low-dimensional structures hidden in high-dimensional data. Somet…

math.ST2018

On the Estimation of Latent Distances Using Graph Distances

Ery Arias-Castro, Antoine Channarond, Bruno Pelletier +1

We are given the adjacency matrix of a geometric graph and the task of recovering the latent positions. We study one of the most popular approaches which consists in using the grap…

math.ST2016

Remember the Curse of Dimensionality: The Case of Goodness-of-Fit Testing in Arbitrary Dimension

Ery Arias-Castro, Bruno Pelletier, Venkatesh Saligrama

Despite a substantial literature on nonparametric two-sample goodness-of-fit testing in arbitrary dimensions spanning decades, there is no mention there of any curse of dimensional…

math.ST2015

On the Consistency of the Crossmatch Test

Ery Arias-Castro, Bruno Pelletier

Rosenbaum (2005) proposed the crossmatch test for two-sample goodness-of-fit testing in arbitrary dimensions. We prove that the test is consistent against all fixed alternatives. I…

stat.ML2012★ 12 cited

On the convergence of maximum variance unfolding

Ery Arias-Castro, Bruno Pelletier

Maximum Variance Unfolding is one of the main methods for (nonlinear) dimensionality reduction. We study its large sample limit, providing specific rates of convergence under stand…

math.ST2010★ 15 cited

The Normalized Graph Cut and Cheeger Constant: from Discrete to Continuous

Ery Arias-Castro, Bruno Pelletier, Pierre Pudlo

Let M be a bounded domain of a Euclidian space with smooth boundary. We relate the Cheeger constant of M and the conductance of a neighborhood graph defined on a random sample from…

stat.ML2010★ 6 cited

Operator norm convergence of spectral clustering on level sets

Bruno Pelletier, Pierre Pudlo

Following Hartigan, a cluster is defined as a connected component of the t-level set of the underlying density, i.e., the set of points for which the density is greater than t. A c…