collaborators

9 papers

stat.ME2026

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…

stat.ME2026

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,…

stat.ME2026

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…

stat.ML2026

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…

stat.ME2025

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…

stat.ML2025

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…