5 citations · 7 across the 11 of their papers we have counts for
14 papers
Distributional Regression for Arbitrary Response Variables Using ReLU Neural Networks
Junpeng Ren, Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla
This paper develops a unified framework for distributional regression with arbitrary response variables. Rather than relying on scalar thresholds or Euclidean structure, we model c…
Optimal detection of general moment changes: Simultaneous mean and covariance change detection and beyond
Xiaokai Luo, Chenghao Xu, Haotian Xu +2
We study multiple change-point detection in multivariate time series whose distributions change in a piecewise constant manner. Distributional changes can manifest across different…
Transfer Learning in Nonparametric Regression with Deep ReLU Networks
Junpeng Ren, Carlos Misael Madrid Padilla, Yanzhen Chen +1
This paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups. Under the assumption that groups share a common stru…
Online Change Point Detection for Multivariate Inhomogeneous Poisson Processes Time Series
Xiaokai Luo, Haotian Xu, Carlos Misael Madrid Padilla +1
We study online change point detection for multivariate inhomogeneous Poisson point process time series. This setting arises commonly in applications such as earthquake seismology,…
Optimal Bias-variance Tradeoff in Matrix and Tensor Estimation
Shivam Kumar, Xiaokai Luo, Haotian Xu +3
We study matrix and tensor denoising when the underlying signal is \textbf{not} necessarily low-rank. In the tensor setting, we observe \[ Y = X^\ast + Z \in \mathbb{R}^{p_1 \times…
Risk Bounds For Distributional Regression
Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla, Sabyasachi Chatterjee
This work examines risk bounds for nonparametric distributional regression estimators. For convex-constrained distributional regression, general upper bounds are established for th…
Robust and Scalable Variational Bayes
Carlos Misael Madrid Padilla, Shitao Fan, Lizhen Lin
We propose a robust and scalable framework for variational Bayes (VB) that effectively handles outliers and contamination of arbitrary nature in large datasets. Our approach divide…
Multivariate Poisson intensity estimation via low-rank tensor decomposition
Haotian Xu, Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla +1
In this work, we propose new matrix- and tensor-based methodologies for estimating multivariate intensity functions of inhomogeneous point processes. By viewing multivariate intens…
Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks
Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla, Yik Lun Kei +2
This paper addresses the problems of conditional variance estimation and confidence interval construction in nonparametric regression using dense networks with the Rectified Linear…
Dense ReLU Neural Networks for Temporal-spatial Model
Carlos Misael Madrid Padilla, Zhi Zhang, Xiaokai Luo +2
In this paper, we focus on fully connected deep neural networks utilizing the Rectified Linear Unit (ReLU) activation function for nonparametric estimation. We derive non-asymptoti…
Spatio-temporal model via Locally Adaptive Regression Splines
Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla, Daren Wang
This paper focuses on the estimation of a non-parametric regression function in the presence of data with spatio-temporal dependencies. In such a context, we study Locally Adaptive…
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…
Change-point Detection for Sparse and Dense Functional Data in General Dimensions
Carlos Misael Madrid Padilla, Daren Wang, Zifeng Zhao +1
We study the problem of change-point detection and localisation for functional data sequentially observed on a general d-dimensional space, where we allow the functional curves to…
A causal fused lasso for interpretable heterogeneous treatment effects estimation
Oscar Hernan Madrid Padilla, Yanzhen Chen, Carlos Misael Madrid Padilla +1
We propose a novel method for estimating heterogeneous treatment effects based on the fused lasso. By first ordering samples based on the propensity or prognostic score, we match u…