Ery Arias-Castro

research

∙ 11/19/2021

An Asymptotic Equivalence between the Mean-Shift Algorithm and the Cluster Tree

Two important nonparametric approaches to clustering emerged in the 1970...

Ery Arias-Castro, et al. ∙

research

∙ 09/17/2021

Level Sets or Gradient Lines? A Unifying View of Modal Clustering

The paper establishes a strong correspondence, if not an equivalence, be...

Ery Arias-Castro, et al. ∙

research

∙ 06/26/2021

Extending the Patra-Sen Approach to Estimating the Background Component in a Two-Component Mixture Model

Patra and Sen (2016) consider a two-component mixture model, where one c...

Ery Arias-Castro, et al. ∙

research

∙ 05/07/2021

From Graph Centrality to Data Depth

Given a sample of points in a Euclidean space, we can define a notion of...

Eddie Aamari, et al. ∙

research

∙ 04/16/2021

Estimation of the Global Mode of a Density: Minimaxity, Adaptation, and Computational Complexity

We consider the estimation of the global mode of a density under some de...

Ery Arias-Castro, et al. ∙

research

∙ 12/14/2020

Template Matching with Ranks

We consider the problem of matching a template to a noisy signal. Motiva...

Ery Arias-Castro, et al. ∙

research

∙ 11/25/2020

Minimax Estimation of Distances on a Surface and Minimax Manifold Learning in the Isometric-to-Convex Setting

We start by considering the problem of estimating intrinsic distances on...

Ery Arias-Castro, et al. ∙

research

∙ 10/19/2020

On the Consistency of Metric and Non-Metric K-medoids

We establish the consistency of K-medoids in the context of metric space...

Ery Arias-Castro, et al. ∙

research

∙ 09/09/2020

Template Matching and Change Point Detection by M-estimation

We consider the fundamental problem of matching a template to a signal. ...

Ery Arias-Castro, et al. ∙

research

∙ 09/07/2020

Anomaly Detection in Stationary Settings: A Permutation-Based Higher Criticism Approach

Anomaly detection when observing a large number of data streams is essen...

Ivo V. Stoepker, et al. ∙

research

∙ 07/09/2020

K-Means and Gaussian Mixture Modeling with a Separation Constraint

We consider the problem of clustering with K-means and Gaussian mixture ...

He Jiang, et al. ∙

research

∙ 06/20/2019

A Multiscale Scan Statistic for Adaptive Submatrix Localization

We consider the problem of localizing a submatrix with larger-than-usual...

Yuchao Liu, et al. ∙

research

∙ 11/17/2018

Detection of Sparse Positive Dependence

In a bivariate setting, we consider the problem of detecting a sparse co...

Ery Arias-Castro, et al. ∙

research

∙ 10/22/2018

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

One of the common tasks in unsupervised learning is dimensionality reduc...

Ery Arias-Castro, et al. ∙

research

∙ 08/02/2018

A Scan Procedure for Multiple Testing

In a multiple testing framework, we propose a method that identifies the...

Shiyun Chen, et al. ∙

research

∙ 07/27/2018

The Sparse Variance Contamination Model

We consider a Gaussian contamination (i.e., mixture) model where the con...

Ery Arias-Castro, et al. ∙

research

∙ 04/27/2018

On the Estimation of Latent Distances Using Graph Distances

We are given the adjacency matrix of a geometric graph and the task of r...

Ery Arias-Castro, et al. ∙

research

∙ 02/23/2018

Detection of Sparse Mixtures: Higher Criticism and Scan Statistic

We consider the problem of detecting a sparse mixture as studied by Ings...

Ery Arias-Castro, et al. ∙

research

∙ 11/30/2017

RANSAC Algorithms for Subspace Recovery and Subspace Clustering

We consider the RANSAC algorithm in the context of subspace recovery and...

Ery Arias-Castro, et al. ∙

research

∙ 06/28/2017

Unconstrained and Curvature-Constrained Shortest-Path Distances and their Approximation

We study shortest paths and their distances on a subset of a Euclidean s...

Ery Arias-Castro, et al. ∙

research

∙ 02/23/2016

A Simple Approach to Sparse Clustering

Consider the problem of sparse clustering, where it is assumed that only...

Ery Arias-Castro, et al. ∙

research

∙ 02/18/2016

A Nonparametric Framework for Quantifying Generative Inference on Neuromorphic Systems

Restricted Boltzmann Machines and Deep Belief Networks have been success...

Ojash Neopane, et al. ∙

research

∙ 08/13/2013

Community Detection in Sparse Random Networks

We consider the problem of detecting a tight community in a sparse rando...

Ery Arias-Castro, et al. ∙

research

∙ 02/28/2013

Community Detection in Random Networks

We formalize the problem of detecting a community in a network into test...

Ery Arias-Castro, et al. ∙

research

∙ 01/09/2013

Spectral Clustering Based on Local PCA

We propose a spectral clustering method based on local principal compone...

Ery Arias-Castro, et al. ∙

research

∙ 08/31/2012

On the convergence of maximum variance unfolding

Maximum Variance Unfolding is one of the main methods for (nonlinear) di...

Ery Arias-Castro, et al. ∙

research

∙ 08/31/2012

A two-stage denoising filter: the preprocessed Yaroslavsky filter

This paper describes a simple image noise removal method which combines ...

Joseph Salmon, et al. ∙

research

∙ 12/19/2011

Oracle inequalities and minimax rates for non-local means and related adaptive kernel-based methods

This paper describes a novel theoretical characterization of the perform...

Ery Arias-Castro, et al. ∙

research

∙ 01/08/2010

Spectral clustering based on local linear approximations

In the context of clustering, we assume a generative model where each cl...

Ery Arias-Castro, et al. ∙

research

∙ 09/12/2009

Clustering Based on Pairwise Distances When the Data is of Mixed Dimensions

In the context of clustering, we consider a generative model in a Euclid...

Ery Arias-Castro, et al. ∙

Ery Arias-Castro

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