3 papers
cs.LG2025
Learning Kronecker-Structured Graphs from Smooth Signals
Changhao Shi, Gal Mishne
Graph learning, or network inference, is a prominent problem in graph signal processing (GSP). GSP generalizes the Fourier transform to non-Euclidean domains, and graph learning is…
stat.ME2024
Adaptive Weighted Random Isolation (AWRI): a simple design to estimate causal effects under network interference
Changhao Shi, Haoyu Yang, Yichen Qin +1
Recently, causal inference under interference has gained increasing attention in the literature. In this paper, we focus on randomized designs for estimating the total treatment ef…
cs.LG2024
Learning Cartesian Product Graphs with Laplacian Constraints
Changhao Shi, Gal Mishne
Graph Laplacian learning, also known as network topology inference, is a problem of great interest to multiple communities. In Gaussian graphical models (GM), graph learning amount…