1 citations · 1 across the 2 of their papers we have counts for
6 papers
AEGCN: An Autoencoder-Constrained Graph Convolutional Network
Mingyuan Ma, Sen Na, Hongyu Wang
We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its…
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees
Sen Na, Yuwei Luo, Zhuoran Yang +2
Graph representation learning is a ubiquitous task in machine learning where the goal is to embed each vertex into a low-dimensional vector space. We consider the bipartite graph a…
Exponential Decay in the Sensitivity Analysis of Nonlinear Dynamic Programming
Sen Na, Mihai Anitescu
In this paper, we study the sensitivity of discrete-time dynamic programs with nonlinear dynamics and objective to perturbations in the initial conditions and reference parameters.…
Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivity
Sen Na, Mladen Kolar, Oluwasanmi Koyejo
Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, thus are of particular interest for scientific i…
High-dimensional Index Volatility Models via Stein's Identity
Sen Na, Mladen Kolar
We study the estimation of the parametric components of single and multiple index volatility models. Using the first- and second-order Stein's identities, we develop methods that a…
High-dimensional Varying Index Coefficient Models via Stein's Identity
Sen Na, Zhuoran Yang, Zhaoran Wang +1
We study the parameter estimation problem for a varying index coefficient model in high dimensions. Unlike the most existing works that iteratively estimate the parameters and link…