H Guo, T Nguyen, A Yadav - ICML Workshop on Algorithmic …, 2021 - amulyayadav.com
This work presents CounterNet, a novel endto-end learning framework which integrates the predictive model training and the counterfactual (CF) explanation into a single end-to-end …
To increase the adoption of counterfactual explanations in practice, several criteria that these should adhere to have been put forward in the literature. We propose counterfactual …
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 …
W Yang, J Li, C Xiong, SCH Hoi - arXiv preprint arXiv:2205.15540, 2022 - arxiv.org
Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods …
Counterfactual explanations were first introduced as a human-centric way to understand model behavior. While validity remains core to the counterfactual explanation definition …
F Yang, SS Alva, J Chen, X Hu - Proceedings of the 27th ACM SIGKDD …, 2021 - dl.acm.org
Counterfactuals, serving as one of the emerging type of model interpretations, have recently received attention from both researchers and practitioners. Counterfactual explanations …
As an essential post-hoc explanatory method, counterfactual explanation enables people to understand and react to machine learning models. Works on counterfactual explanation …
HD Kim, YJ Ju, JH Hong, SW Lee - Information Sciences, 2024 - Elsevier
Counterfactual explanation (CFE) provides actionable counterexamples and enhances the interpretability of the decision boundaries in deep neural networks and thereby has gained …
C Wang, XH Li, H Han, S Wang, L Wang… - Proceedings of the 27th …, 2021 - dl.acm.org
Deep learning has shown powerful performances in many fields, however its black-box nature hinders its further applications. In response, explainable artificial intelligence …