The Robustness of Counterfactual Explanations Over Time (original) (raw)
Related papers
Counterfactual Explanations for Machine Learning: Challenges Revisited
2021
ReLACE: Reinforcement Learning Agent for Counterfactual Explanations of Arbitrary Predictive Models
ArXiv, 2021
ArXiv, 2020
FCE: Feedback Based Counterfactual Explanations for Explainable AI
IEEE Access
Information Fusion, 2021
Counterfactual Models for Fair and Adequate Explanations
Machine Learning and Knowledge Extraction, 2022
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations
2020
Counterfactual Explanations Using Optimization With Constraint Learning
2022
ViCE: Visual Counterfactual Explanations for Machine Learning Models
2020
Truthful Meta-Explanations for Local Interpretability of Machine Learning Models
arXiv (Cornell University), 2022
Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders
Applied Sciences
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems
arXiv (Cornell University), 2022
Stable and actionable explanations of black-box models through factual and counterfactual rules
Data Mining and Knowledge Discovery, 2022
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
arXiv (Cornell University), 2023
On the Robustness of Counterfactual Explanations to Adverse Perturbations
ArXiv, 2022
Proposed Guidelines for the Responsible Use of Explainable Machine Learning
2019
Multi-Class Counterfactual Explanations using Support Vector Data Description
IEEE transactions on artificial intelligence, 2022
Explaining Explanations: An Overview of Interpretability of Machine Learning
2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)
Pitfalls of Explainable ML: An Industry Perspective
ArXiv, 2021
Multi-Objective Counterfactual Explanations
Parallel Problem Solving from Nature – PPSN XVI
Analyzing the Impact of Adversarial Examples on Explainable Machine Learning
arXiv (Cornell University), 2023
Feature Attributions and Counterfactual Explanations Can Be Manipulated
ArXiv, 2021
Explainable Artificial Intelligence in Machine Learning
Capstone Project Report, 2024
The Uncertainty of Counterfactuals in Deep Learning
The International FLAIRS Conference Proceedings
Explainable Machine Learning with Prior Knowledge: An Overview
ArXiv, 2021
Explainable Artificial Intelligence: How Subsets of the Training Data Affect a Prediction
ArXiv, 2020
Conceptual challenges for interpretable machine learning
Synthese, 2022
Finding Regions of Counterfactual Explanations via Robust Optimization
arXiv (Cornell University), 2023
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
ArXiv, 2020
Declarative Approaches to Counterfactual Explanations for Classification
Theory and Practice of Logic Programming
Counterfactual building and evaluation via eXplainable Support Vector Data Description
IEEE Access
Counterfactual Explanations as Interventions in Latent Space
2021
Individual Explanations in Machine Learning Models: A Survey for Practitioners
ArXiv, 2021
Robust and Stable Black Box Explanations
2020
Foiling Explanations in Deep Neural Networks
Cornell University - arXiv, 2022