1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Limits, approximation and size transferability for GNNs on sparse graphs via graphops
Thien Le, Stefanie Jegelka
Can graph neural networks generalize to graphs that are different from the graphs they were trained on, e.g., in size? In this work, we study this question from a theoretical persp…
stat.ME2022
Testing the Graph of a Gaussian Graphical Model
Thien-Minh Le, Ping-Shou Zhong, Chenlei Leng
The Gaussian graphical model is routinely employed to model the joint distribution of multiple random variables. The graph it induces is not only useful for describing the relation…
cs.LG2022★ 1 cited
Training invariances and the low-rank phenomenon: beyond linear networks
Thien Le, Stefanie Jegelka
The implicit bias induced by the training of neural networks has become a topic of rigorous study. In the limit of gradient flow and gradient descent with appropriate step size, it…