21 citations · 41 across the 10 of their papers we have counts for
21 papers
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…
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…