Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. - Kjersti Aas; Martin Jullum; Anders Løland (Artif. Intell., 2021)

Explaining predictive models using Shapley values and non-parametric vine copulas. - Kjersti Aas; Thomas Nagler; Martin Jullum; Anders Løland (CoRR, 2021)

Statistical embedding: Beyond principal components. - Dag Tjøstheim; Martin Jullum; Anders Løland (CoRR, 2021)

MCCE: Monte Carlo sampling of realistic counterfactual explanations. - Annabelle Redelmeier; Martin Jullum; Kjersti Aas; Anders Løland (CoRR, 2021)

eXplego: An interactive Tool that Helps you Select Appropriate XAI-methods for your Explainability Needs. - Martin Jullum; Jacob Sjødin; Robindra Prabhu; Anders Løland (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2023)

MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data. - Annabelle Redelmeier; Martin Jullum; Kjersti Aas; Anders Løland (Data Min. Knowl. Discov., 2024)

AI/ML for 5G and Beyond Cybersecurity. - Sandeep Pirbhulal; Habtamu Abie; Martin Jullum; Didrik Nielsen; Anders Løland (CoRR, 2025)