28 citations · 47 across the 5 of their papers we have counts for
5 papers
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Ziheng Chen, Fabrizio Silvestri, Jia Wang +4
Recently, graph neural networks (GNNs) have been widely used to develop successful recommender systems. Although powerful, it is very difficult for a GNN-based recommender system t…
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
Ziheng Chen, Fabrizio Silvestri, Jia Wang +3
Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods eithe…
Costly Features Classification using Monte Carlo Tree Search
Ziheng Chen, Jin Huang, Hongshik Ahn +1
We consider the problem of costly feature classification, where we sequentially select the subset of features to make a balance between the classification error and the feature cos…
Modeling and Computation of High Efficiency and Efficacy Multi-Step Batch Testing for Infectious Diseases
Hongshik Ahn, Haoran Jiang, Xiaolin Li
We propose a mathematical model based on probability theory to optimize COVID-19 testing by a multi-step batch testing approach with variable batch sizes. This model and simulation…
Item Response Theory based Ensemble in Machine Learning
Ziheng Chen, Hongshik Ahn
In this article, we propose a novel probabilistic framework to improve the accuracy of a weighted majority voting algorithm. In order to assign higher weights to the classifiers wh…