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Found 11 papers, 3 papers with code
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
We establish a theoretical framework for probabilistically defining robustness to model change and demonstrate how our BetaRCE method directly stems from it.
A-PETE: Adaptive Prototype Explanations of Tree Ensembles
The need for interpreting machine learning models is addressed through prototype explanations within the context of tree ensembles.
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
By offering a cohesive solution to the optimization and plausibility challenges in GCEs, our work significantly advances the interpretability and accountability of AI models, marking a step forward in the pursuit of transparent AI.
Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization of plausibility within a unified framework.
A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining the more desired predictions.
The Problem of Coherence in Natural Language Explanations of Recommendations
Providing natural language explanations for recommendations is particularly useful from the perspective of a non-expert user.
Deep Similarity Learning Loss Functions in Data Transformation for Class Imbalance
Improving the classification of multi-class imbalanced data is more difficult than its two-class counterpart.
Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues
Reproducibility is one of the core dimensions that concur to deliver Trustworthy Artificial Intelligence.
The Influence of Multiple Classes on Learning Online Classifiers from Imbalanced and Concept Drifting Data Streams
This work is aimed at the experimental studying the influence of local data characteristics and drifts on the difficulties of learning various online classifiers from multi-class imbalanced data streams.
Quality versus speed in energy demand prediction for district heating systems
In this paper, we evaluate the above methods with respect to the quality and computational costs, both in the training and in the execution.
