45 citations · 148 across the 48 of their papers we have counts for
53 papers
Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
Marcos Matabuena, Xiaokai Luo, Oscar Hernan Madrid Padilla
Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains. Howe…
Two-Stage Score Estimation for Group-Specific Diffusion Models
Huitao Yang, Oscar Hernan Madrid Padilla
Generative modeling across related distributions raises a question: how can shared structure support accurate generation for a target distribution with limited observations? We dev…
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…
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,…
Decoder-only Clustering in Attributed Graphs
Yik Lun Kei, Oscar Hernan Madrid Padilla, Rebecca Killick +3
This manuscript studies nodal clustering in graphs having multivariate attributes at each node. The framework includes node-specific priors for low-dimensional representations, cou…
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…
A Bayesian framework for change-point detection with uncertainty quantification
Davis Berlind, Lorenzo Cappello, Oscar Hernan Madrid Padilla
We introduce a novel Bayesian method that can detect multiple structural breaks in the mean and variance of a length time-series. Our method quantifies uncertainty by returning…
Change Point Localization and Inference in Dynamic Multilayer Networks
Fan Wang, Kyle Ritscher, Yik Lun Kei +2
We study offline change point localization and inference in dynamic multilayer random dot product graphs (D-MRDPGs), where at each time point, a multilayer network is observed with…
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi +2
Depth measures are powerful tools for defining level sets in emerging, non--standard, and complex random objects such as high-dimensional multivariate data, functional data, and ra…
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…
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…
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory
Zhi Zhang, Chris Chow, Yasi Zhang +7
Lifelong reinforcement learning (RL) has been developed as a paradigm for extending single-task RL to more realistic, dynamic settings. In lifelong RL, the "life" of an RL agent is…
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…
Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi +2
Depth measures have gained popularity in the statistical literature for defining level sets in complex data structures like multivariate data, functional data, and graphs. Despite…
Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model
Yik Lun Kei, Jialiang Li, Hangjian Li +2
This manuscript studies the unsupervised change point detection problem in time series of graphs using a decoder-only latent space model. The proposed framework consists of learnab…
Conditional Mean and Variance Estimation via \textit{k}-NN Algorithm with Automated Variance Selection
Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla +1
We introduce a novel \textit{k}-nearest neighbor (\textit{k}-NN) regression method for joint estimation of the conditional mean and variance. The proposed algorithm preserves the 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…
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…
Multilayer random dot product graphs: Estimation and online change point detection
Fan Wang, Wanshan Li, Oscar Hernan Madrid Padilla +2
We study the multilayer random dot product graph (MRDPG) model, an extension of the random dot product graph to multilayer networks. To estimate the edge probabilities, we deploy a…
Change Point Detection on A Separable Model for Dynamic Networks
Yik Lun Kei, Hangjian Li, Yanzhen Chen +1
This paper studies the unsupervised change point detection problem in time series of networks using the Separable Temporal Exponential-family Random Graph Model (STERGM). Inherentl…
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…