101 citations · 243 across the 11 of their papers we have counts for
11 papers
How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
Debargha Ghosh, Silja Renooij, Anna V. Kononova
Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference. However, its behaviour u…
Parameterized Argumentation-based Reasoning Tasks for Benchmarking Generative Language Models
Cor Steging, Silja Renooij, Bart Verheij
Generative large language models as tools in the legal domain have the potential to improve the justice system. However, the reasoning behavior of current generative models is brit…
Discovering the Rationale of Decisions: Experiments on Aligning Learning and Reasoning
Cor Steging, Silja Renooij, Bart Verheij
In AI and law, systems that are designed for decision support should be explainable when pursuing justice. In order for these systems to be fair and responsible, they should make c…
How to Elicit Many Probabilities
Linda C. van der Gaag, Silja Renooij, Cilia L. M. Witteman +2
In building Bayesian belief networks, the elicitation of all probabilities required can be a major obstacle. We learned the extent of this often-cited observation in the constructi…
Enhancing QPNs for Trade-off Resolution
Silja Renooij, Linda C. van der Gaag
Qualitative probabilistic networks have been introduced as qualitative abstractions of Bayesian belief networks. One of the major drawbacks of these qualitative networks is their c…
Pivotal Pruning of Trade-offs in QPNs
Silja Renooij, Linda C. van der Gaag, Simon Parsons +1
Qualitative probabilistic networks have been designed for probabilistic reasoning in a qualitative way. Due to their coarse level of representation detail, qualitative probabilisti…