15 citations · 36 across the 4 of their papers we have counts for
5 papers
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Lisa Schut, Oscar Key, Rory McGrath +4
Counterfactual explanations (CEs) are a practical tool for demonstrating why machine learning classifiers make particular decisions. For CEs to be useful, it is important that they…
Background Knowledge Injection for Interpretable Sequence Classification
Severin Gsponer, Luca Costabello, Chan Le Van +4
Sequence classification is the supervised learning task of building models that predict class labels of unseen sequences of symbols. Although accuracy is paramount, in certain scen…
Knowledge Graph Embeddings and Explainable AI
Federico Bianchi, Gaetano Rossiello, Luca Costabello +2
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we intr…
Probability Calibration for Knowledge Graph Embedding Models
Pedro Tabacof, Luca Costabello
Knowledge graph embedding research has overlooked the problem of probability calibration. We show popular embedding models are indeed uncalibrated. That means probability estimates…
Interpretable Credit Application Predictions With Counterfactual Explanations
Rory Mc Grath, Luca Costabello, Chan Le Van +4
We predict credit applications with off-the-shelf, interchangeable black-box classifiers and we explain single predictions with counterfactual explanations. Counterfactual explanat…