Ery Arias-Castro

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University of California, San Diego

Professor in Department of Mathematics at University of California, San Diego

Template Matching with Ranks

We consider the problem of matching a template to a noisy signal. Motiva...
12/14/2020 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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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...
11/25/2020 ∙ by Ery Arias-Castro, et al. ∙ 4 ∙ share

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

We establish the consistency of K-medoids in the context of metric space...
10/19/2020 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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Template Matching and Change Point Detection by M-estimation

We consider the fundamental problem of matching a template to a signal. ...
09/09/2020 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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

Anomaly detection when observing a large number of data streams is essen...
09/07/2020 ∙ by Ivo V. Stoepker, et al. ∙ 0 ∙ share

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K-Means and Gaussian Mixture Modeling with a Separation Constraint

We consider the problem of clustering with K-means and Gaussian mixture ...
07/09/2020 ∙ by He Jiang, et al. ∙ 0 ∙ share

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A Multiscale Scan Statistic for Adaptive Submatrix Localization

We consider the problem of localizing a submatrix with larger-than-usual...
06/20/2019 ∙ by Yuchao Liu, et al. ∙ 0 ∙ share

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Detection of Sparse Positive Dependence

In a bivariate setting, we consider the problem of detecting a sparse co...
11/17/2018 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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

One of the common tasks in unsupervised learning is dimensionality reduc...
10/22/2018 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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A Scan Procedure for Multiple Testing

In a multiple testing framework, we propose a method that identifies the...
08/02/2018 ∙ by Shiyun Chen, et al. ∙ 0 ∙ share

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The Sparse Variance Contamination Model

We consider a Gaussian contamination (i.e., mixture) model where the con...
07/27/2018 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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On the Estimation of Latent Distances Using Graph Distances

We are given the adjacency matrix of a geometric graph and the task of r...
04/27/2018 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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Detection of Sparse Mixtures: Higher Criticism and Scan Statistic

We consider the problem of detecting a sparse mixture as studied by Ings...
02/23/2018 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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RANSAC Algorithms for Subspace Recovery and Subspace Clustering

We consider the RANSAC algorithm in the context of subspace recovery and...
11/30/2017 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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

We study shortest paths and their distances on a subset of a Euclidean s...
06/28/2017 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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A Simple Approach to Sparse Clustering

Consider the problem of sparse clustering, where it is assumed that only...
02/23/2016 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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A Nonparametric Framework for Quantifying Generative Inference on Neuromorphic Systems

Restricted Boltzmann Machines and Deep Belief Networks have been success...
02/18/2016 ∙ by Ojash Neopane, et al. ∙ 0 ∙ share

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Community Detection in Sparse Random Networks

We consider the problem of detecting a tight community in a sparse rando...
08/13/2013 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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Community Detection in Random Networks

We formalize the problem of detecting a community in a network into test...
02/28/2013 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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Spectral Clustering Based on Local PCA

We propose a spectral clustering method based on local principal compone...
01/09/2013 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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On the convergence of maximum variance unfolding

Maximum Variance Unfolding is one of the main methods for (nonlinear) di...
08/31/2012 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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A two-stage denoising filter: the preprocessed Yaroslavsky filter

This paper describes a simple image noise removal method which combines ...
08/31/2012 ∙ by Joseph Salmon, et al. ∙ 0 ∙ share

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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...
12/19/2011 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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Spectral clustering based on local linear approximations

In the context of clustering, we assume a generative model where each cl...
01/08/2010 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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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...
09/12/2009 ∙ by Ery Arias-Castro, et al. ∙ 0 ∙ share

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Ery Arias-Castro

Featured Co-authors

Jue Wang

Teng Zhang

Gilad Lerman

Rebecca Willett

Joseph Salmon

Adel Javanmard

He Jiang

Thibaut Le Gouic

Nicolas Verzelen

Srinjoy Das

Rong Huang

Template Matching with Ranks

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

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

Template Matching and Change Point Detection by M-estimation

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

K-Means and Gaussian Mixture Modeling with a Separation Constraint

A Multiscale Scan Statistic for Adaptive Submatrix Localization

Detection of Sparse Positive Dependence

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

A Scan Procedure for Multiple Testing

The Sparse Variance Contamination Model

On the Estimation of Latent Distances Using Graph Distances

Detection of Sparse Mixtures: Higher Criticism and Scan Statistic

RANSAC Algorithms for Subspace Recovery and Subspace Clustering

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

A Simple Approach to Sparse Clustering

A Nonparametric Framework for Quantifying Generative Inference on Neuromorphic Systems

Community Detection in Sparse Random Networks

Community Detection in Random Networks

Spectral Clustering Based on Local PCA

On the convergence of maximum variance unfolding

A two-stage denoising filter: the preprocessed Yaroslavsky filter

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

Spectral clustering based on local linear approximations

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