4 papers
Tractable Shapley Values and Interactions via Tensor Networks
Farzaneh Heidari, Chao Li, Guillaume Rabusseau
We show how to replace the O(2^n) coalition enumeration over n features behind Shapley values and Shapley-style interaction indices with a few-evaluation scheme on a tensor-network…
Locally Private Subgraph Counting via Noisy Adjacency Matrix and Differential Privacy on Randomized Data
Jintao Guo, Ying Zhou, Chao Li +2
Subgraph counting is a fundamental primitive for graph analytics, with applications ranging from social recommendation to anomaly detection. Private subgraph counting under edge lo…
Graph-Based Uncertainty-Aware Self-Training with Stochastic Node Labeling
Tom Liu, Anna Wu, Chao Li
Self-training has become a popular semi-supervised learning technique for leveraging unlabeled data. However, the over-confidence of pseudo-labels remains a key challenge. In this…
Uncertainty-Aware Graph Self-Training with Expectation-Maximization Regularization
Emily Wang, Michael Chen, Chao Li
In this paper, we propose a novel \emph{uncertainty-aware graph self-training} approach for semi-supervised node classification. Our method introduces an Expectation-Maximization (…