shapr: An R-package for explaining machine learning models with dependence-aware Shapley values. - Nikolai Sellereite; Martin Jullum (J. Open Source Softw., 2019)

Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees. - Annabelle Redelmeier; Martin Jullum; Kjersti Aas (CD-MAKE, 2020)

Comparison of Contextual Importance and Utility with LIME and Shapley Values. - Kary Främling; Marcus Westberg; Martin Jullum; Manik Madhikermi; Avleen Malhi (EXTRAAMAS@AAMAS, 2021)

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)

groupShapley: Efficient prediction explanation with Shapley values for feature groups. - Martin Jullum; Annabelle Redelmeier; Kjersti Aas (CoRR, 2021)

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

Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features. - Lars H. B. Olsen; Ingrid Kristine Glad; Martin Jullum; Kjersti Aas (J. Mach. Learn. Res., 2022)

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)

A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them. - Lars Henry Berge Olsen; Ingrid Kristine Glad; Martin Jullum; Kjersti Aas (CoRR, 2023)

Finding Money Launderers Using Heterogeneous Graph Neural Networks. - Fredrik Johannessen; Martin Jullum (CoRR, 2023)

Improving the Sampling Strategy in KernelSHAP. - Lars Henry Berge Olsen; Martin Jullum (CoRR, 2024)

A comparative study of methods for estimating model-agnostic Shapley value explanations. - Lars Henry Berge Olsen; Ingrid Kristine Glad; Martin Jullum; Kjersti Aas (Data Min. Knowl. Discov., 2024)

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)

shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python. - Martin Jullum; Lars Henry Berge Olsen; Jon Lachmann; Annabelle Redelmeier (CoRR, 2025)

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models. - Jan Kapar; Niklas Koenen; Martin Jullum (CoRR, 2025)

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