45 citations · 148 across the 48 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
Learning Gaussian DAGs from Network Data
Hangjian Li, Oscar Hernan Madrid Padilla, Qing Zhou
Structural learning of directed acyclic graphs (DAGs) or Bayesian networks has been studied extensively under the assumption that data are independent. We propose a new Gaussian DA…
Distributed Cartesian Power Graph Segmentation for Graphon Estimation
Shitong Wei, Oscar Hernan Madrid-Padilla, James Sharpnack
We study an extention of total variation denoising over images to over Cartesian power graphs and its applications to estimating non-parametric network models. The power graph fuse…
Vector-Space Markov Random Fields via Exponential Families
Wesley Tansey, Oscar Hernan Madrid Padilla, Arun Sai Suggala +1
We present Vector-Space Markov Random Fields (VS-MRFs), a novel class of undirected graphical models where each variable can belong to an arbitrary vector space. VS-MRFs generalize…