Achieving diversity in counterfactual explanations: a review and discussion
In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a
user the predictions of a trained decision model by indicating the modifications to be made …
user the predictions of a trained decision model by indicating the modifications to be made …
Uncertainty quantification with pre-trained language models: A large-scale empirical analysis
Pre-trained language models (PLMs) have gained increasing popularity due to their
compelling prediction performance in diverse natural language processing (NLP) tasks …
compelling prediction performance in diverse natural language processing (NLP) tasks …
Gradient-based uncertainty attribution for explainable bayesian deep learning
Predictions made by deep learning models are prone to data perturbations, adversarial
attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critical to …
attacks, and out-of-distribution inputs. To build a trusted AI system, it is therefore critical to …
Explaining predictive uncertainty with information theoretic shapley values
Researchers in explainable artificial intelligence have developed numerous methods for
helping users understand the predictions of complex supervised learning models. By …
helping users understand the predictions of complex supervised learning models. By …
GLOBE-CE: A translation based approach for global counterfactual explanations
Counterfactual explanations have been widely studied in explainability, with a range of
application dependent methods prominent in fairness, recourse and model understanding …
application dependent methods prominent in fairness, recourse and model understanding …
Bayesian hierarchical models for counterfactual estimation
N Raman, D Magazzeni… - … Conference on Artificial …, 2023 - proceedings.mlr.press
Counterfactual explanations utilize feature perturbations to analyze the outcome of an
original decision and recommend an actionable recourse. We argue that it is beneficial to …
original decision and recommend an actionable recourse. We argue that it is beneficial to …
Global counterfactual explanations: Investigations, implementations and improvements
Counterfactual explanations have been widely studied in explainability, with a range of
application dependent methods emerging in fairness, recourse and model understanding …
application dependent methods emerging in fairness, recourse and model understanding …
X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images
SAR image simulation has attracted much attention due to its great potential to supplement
the scarce training data for deep learning algorithms. Consequently, evaluating the quality of …
the scarce training data for deep learning algorithms. Consequently, evaluating the quality of …
Optimization-Based Uncertainty Attribution Via Learning Informative Perturbations
Uncertainty attribution (UA) aims to identify key contributors to predictive uncertainty in deep
learning models. To improve the faithfulness of existing UA methods, we formulate UA as an …
learning models. To improve the faithfulness of existing UA methods, we formulate UA as an …
Counterfactual explanation with missing values
Counterfactual Explanation (CE) is a post-hoc explanation method that provides a
perturbation for altering the prediction result of a classifier. Users can interpret the …
perturbation for altering the prediction result of a classifier. Users can interpret the …