19 citations · 72 across the 23 of their papers we have counts for
5 papers · 1 filter
Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison
Lucas Vogels, Reza Mohammadi, Marit Schoonhoven +1
Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional depe…
Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
Reza Mohammadi, Marit Schoonhoven, Lucas Vogels +1
Bayesian methods for learning Gaussian graphical models offer a principled framework for quantifying model uncertainty and incorporating prior knowledge. However, their scalability…
Semantic match: Debugging feature attribution methods in XAI for healthcare
Giovanni Cinà, Tabea E. Röber, Rob Goedhart +1
The recent spike in certified Artificial Intelligence (AI) tools for healthcare has renewed the debate around adoption of this technology. One thread of such debate concerns Explai…
Differentially Private Distributed Bayesian Linear Regression with MCMC
Barış Alparslan, Sinan Yıldırım, Ş. İlker Birbil
We propose a novel Bayesian inference framework for distributed differentially private linear regression. We consider a distributed setting where multiple parties hold parts of the…
Finding Regions of Counterfactual Explanations via Robust Optimization
Donato Maragno, Jannis Kurtz, Tabea E. Röber +3
Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a m…