Search Results for author:
Found 13 papers, 6 papers with code
Veni, Vidi, Vici: Solving the Myriad of Challenges before Knowledge Graph Learning
Knowledge Graphs (KGs) have become increasingly common for representing large-scale linked data.
KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation
To ensure faithfulness, each surrogate is trained by distilling knowledge from the original KGE model.
Robust Explanation Constraints for Neural Networks
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.
Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples
We study the problem of explaining link predictions in the Knowledge Graph Embedding (KGE) models.
Machine Learning-Assisted Recurrence Prediction for Early-Stage Non-Small-Cell Lung Cancer Patients
Conclusions: Our results show that machine learning models trained on tabular and graph data can enable objective, personalised and reproducible prediction of relapse and therefore, disease outcome in patients with early-stage NSCLC.
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns
We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs.
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods
These attacks craft adversarial additions or deletions at training time to cause model failure at test time.
Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes
Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, ranging from genetic data to social networks.
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Counterfactual explanations (CEs) are a practical tool for demonstrating why machine learning classifiers make particular decisions.
Background Knowledge Injection for Interpretable Sequence Classification
Sequence classification is the supervised learning task of building models that predict class labels of unseen sequences of symbols.
Knowledge Graph Embeddings and Explainable AI
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces.
Probability Calibration for Knowledge Graph Embedding Models
We show popular embedding models are indeed uncalibrated.
Calibration for Link Prediction
Knowledge Graph Embedding
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Interpretable Credit Application Predictions With Counterfactual Explanations
Our contribution is two-fold: i) we propose positive counterfactuals, i. e. we adapt counterfactual explanations to also explain accepted loan applications, and ii) we propose two weighting strategies to generate more interpretable counterfactuals.
