Martin Jullum

According to our database, Martin Jullum authored at least 15 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

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., July, 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., July, 2024

Improving the Sampling Strategy in KernelSHAP.

Lars Henry Berge Olsen, Martin Jullum

CoRR, 2024

2023

Finding Money Launderers Using Heterogeneous Graph Neural Networks.

Fredrik Johannessen, Martin Jullum

CoRR, 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

eXplego: An interactive Tool that Helps you Select Appropriate XAI-methods for your Explainability Needs.

Martin Jullum, Jacob Sjødin, Robindra Prabhu, Anders Løland

Proceedings of the Joint Proceedings of the xAI-2023 Late-breaking Work, 2023

2022

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

2021

MCCE: Monte Carlo sampling of realistic counterfactual explanations.

Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland

CoRR, 2021

groupShapley: Efficient prediction explanation with Shapley values for feature groups.

Martin Jullum, Annabelle Redelmeier, Kjersti Aas

CoRR, 2021

Statistical embedding: Beyond principal components.

Dag Tjøstheim, Martin Jullum, Anders Løland

CoRR, 2021

Explaining predictive models using Shapley values and non-parametric vine copulas.

Kjersti Aas, Thomas Nagler, Martin Jullum, Anders Løland

CoRR, 2021

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values.

Kjersti Aas, Martin Jullum, Anders Løland

Artif. Intell., 2021

Comparison of Contextual Importance and Utility with LIME and Shapley Values.

Kary Främling, Marcus Westberg, Martin Jullum, Manik Madhikermi, Avleen Malhi

Proceedings of the Explainable and Transparent AI and Multi-Agent Systems, 2021

2020

Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees.

Annabelle Redelmeier, Martin Jullum, Kjersti Aas

Proceedings of the Machine Learning and Knowledge Extraction, 2020

2019

shapr: An R-package for explaining machine learning models with dependence-aware Shapley values.

Nikolai Sellereite, Martin Jullum

J. Open Source Softw., 2019