45 citations · 148 across the 48 of their papers we have counts for
10 papers · 1 filter
Network two-sample test for block models
Chung Kyong Nguen, Oscar Hernan Madrid Padilla, Arash A. Amini
We consider the two-sample testing problem for networks, where the goal is to determine whether two sets of networks originated from the same stochastic model. Assuming no vertex c…
Quantile Additive Trend Filtering
Zhi Zhang, Kyle Ritscher, Oscar Hernan Madrid Padilla
This paper investigates risk bounds for quantile additive trend filtering, a method gaining increasing significance in the realms of additive trend filtering and quantile regressio…
Change point detection and inference in multivariable nonparametric models under mixing conditions
Carlos Misael Madrid Padilla, Haotian Xu, Daren Wang +2
This paper studies multivariate nonparametric change point localization and inference problems. The data consists of a multivariate time series with potentially short range depende…
Variance estimation in graphs with the fused lasso
Oscar Hernan Madrid Padilla
We study the problem of variance estimation in general graph-structured problems. First, we develop a linear time estimator for the homoscedastic case that can consistently estimat…
Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients
Fan Wang, Oscar Hernan Madrid Padilla, Yi Yu +1
We study the theoretical properties of the fused lasso procedure originally proposed by \cite{tibshirani2005sparsity} in the context of a linear regression model in which the regre…
Optimal partition recovery in general graphs
Yi Yu, Oscar Hernan Madrid Padilla, Alessandro Rinaldo
We consider a graph-structured change point problem in which we observe a random vector with piecewise constant but unknown mean and whose independent, sub-Gaussian coordinates cor…
Lattice partition recovery with dyadic CART
Oscar Hernan Madrid Padilla, Yi Yu, Alessandro Rinaldo
We study piece-wise constant signals corrupted by additive Gaussian noise over a -dimensional lattice. Data of this form naturally arise in a host of applications, and the tasks…
Optimal network online change point localisation
Yi Yu, Oscar Hernan Madrid Padilla, Daren Wang +1
We study the problem of online network change point detection. In this setting, a collection of independent Bernoulli networks is collected sequentially, and the underlying distrib…
Quantile regression with deep ReLU Networks: Estimators and minimax rates
Oscar Hernan Madrid Padilla, Wesley Tansey, Yanzhen Chen
Quantile regression is the task of estimating a specified percentile response, such as the median, from a collection of known covariates. We study quantile regression with rectifie…
The DFS Fused Lasso: Linear-Time Denoising over General Graphs
Oscar Hernan Madrid Padilla, James G. Scott, James Sharpnack +1
The fused lasso, also known as (anisotropic) total variation denoising, is widely used for piecewise constant signal estimation with respect to a given undirected graph. The fused…