28 citations · 43 across the 5 of their papers we have counts for
5 papers
Debiasing Machine Unlearning with Counterfactual Examples
Ziheng Chen, Jia Wang, Jun Zhuang +7
The right to be forgotten (RTBF) seeks to safeguard individuals from the enduring effects of their historical actions by implementing machine-learning techniques. These techniques…
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples
Ziheng Chen, Fabrizio Silvestri, Jia Wang +2
Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial int…
Revolutionizing Genomics with Reinforcement Learning Techniques
Mohsen Karami, Khadijeh Jahanian, Khadijeh +10
In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of…
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…