86 citations · 157 across the 11 of their papers we have counts for
7 papers · 1 filter
Robust Explanation Constraints for Neural Networks
Matthew Wicker, Juyeon Heo, Luca Costabello +1
Post-hoc explanation methods are used with the intent of providing insights about neural networks and are sometimes said to help engender trust in their outputs. However, popular e…
Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples
Adrianna Janik, Luca Costabello
We study the problem of explaining link predictions in the Knowledge Graph Embedding (KGE) models. We propose an example-based approach that exploits the latent space representatio…
Machine Learning-Assisted Recurrence Prediction for Early-Stage Non-Small-Cell Lung Cancer Patients
Adrianna Janik, Maria Torrente, Luca Costabello +14
Background: Stratifying cancer patients according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to utili…
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns
Peru Bhardwaj, John Kelleher, Luca Costabello +1
We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models,…
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods
Peru Bhardwaj, John Kelleher, Luca Costabello +1
Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour. We study data poison…
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…