7 citations · 11 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
A Partially Separable Model for Dynamic Valued Networks
Yik Lun Kei, Yanzhen Chen, Oscar Hernan Madrid Padilla
The Exponential-family Random Graph Model (ERGM) is a powerful model to fit networks with complex structures. However, for dynamic valued networks whose observations are matrices o…
2D score based estimation of heterogeneous treatment effects
Steven Siwei Ye, Yanzhen Chen, Oscar Hernan Madrid Padilla
Statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off between accuracy…
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…
Adaptive Non-Parametric Regression With the -NN Fused Lasso
Oscar Hernan Madrid Padilla, James Sharpnack, Yanzhen Chen +1
The fused lasso, also known as total-variation denoising, is a locally-adaptive function estimator over a regular grid of design points. In this paper, we extend the fused lasso to…