Publications

Last updated April 10th, 2024

Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland (2024). MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data. Data Mining and Knowledge Discovery.

Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas (2024). A comparative study of methods for estimating model-agnostic Shapley value explanations. Data Mining and Knowledge Discovery.

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

Dag Tjøstheim, Martin Jullum, Anders Løland (2023). Statistical Embedding: Beyond Principal Components. Statistical Science.

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Dag Tjøstheim, Martin Jullum, Anders Løland (2023). Some recent trends in embeddings of time series and dynamic networks. Journal of Time Series Analysis.

Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas (2022). Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features. Journal of Machine Learning Research.

Øyvind Grotmol, Martin Jullum, Kjersti Aas, Michael Scheuerer (2022). Performance evaluation of volatility estimation methods for Exabel. arXiv preprint arXiv:2203.12402.

Øyvind Grotmol, Michael Scheuerer, Kjersti Aas, Martin Jullum (2022). Exabel's Factor Model. arXiv preprint arXiv:2203.12408.

Martin Jullum, Annabelle Redelmeier, Kjersti Aas (2021). groupShapley: Efficient prediction explanation with Shapley values for feature groups. arXiv preprint arXiv:2106.12228.

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

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

Martin Jullum, Annabelle Redelmeier, Kjersti Aas (2021). Efficient and simple prediction explanations with groupShapley: A practical perspective. Italian Workshop on Explainable Artificial Intelligence 2021.

Kary Främling, Marcus Westberg, Martin Jullum, Manik Madhikermi, Avleen Malhi (2021). Comparison of Contextual Importance and Utility with LIME and Shapley Values. International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems.

Nikolai Sellereite, Martin Jullum (2020). shapr: An R-package for explaining machine learning models with dependence-aware Shapley values. The Journal of Open Source Software.

Håkon Otneim, Martin Jullum, Dag Tjøstheim (2020). Pairwise local Fisher and Naı̈ve Bayes: Improving two standard discriminants. Journal of Econometrics.

Martin Jullum (2020). Investigating mesh-based approximation methods for the normalization constant in the log Gaussian Cox process likelihood. Stat.

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Annabelle Redelmeier, Martin Jullum, Kjersti Aas (2020). Explaining predictive models with mixed features using Shapley values and conditional inference trees. International Cross-Domain Conference for Machine Learning and Knowledge Extraction.

Martin Jullum, Thordis Thorarinsdottir, Fabian E Bachl (2020). Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling. Journal of the Royal Statistical Society: Series C (Applied Statistics).

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Martin Jullum, Anders Løland, Ragnar Bang Huseby, Geir Ånonsen, Johannes Lorentzen (2020). Detecting money laundering transactions with machine learning. Journal of Money Laundering Control.

Martin Jullum, Nils Lid Hjort (2019). What price semiparametric Cox regression?. Lifetime data analysis.

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Lars Holden, Martin Jullum, Geir Kjetil Sandve (2017). Statistical modeling of repertoire overlap in entire sampling spaces. NR-note SAMBA/13/17, Norwegian Computing Center.

Martin Jullum, Nils Lid Hjort (2017). Parametric or nonparametric: The FIC approach. Statistica Sinica.

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Gudmund Hermansen, Nils Lid Hjort, Martin Jullum (2016). Parametric or nonparametric: The FIC approach for stationary time series. Technical Report, Department of Mathematics, University of Oslo.

Martin Jullum (2016). New focused approaches to topics within model selection and approximate Bayesian inversion.

Odd Kolbjønsen, Arild Buland, Ragnar Hauge, Per Røe, Martin Jullum, Richard William Metcalfe, Øyvind Skjæveland (2016). Bayesian AVO inversion to rock properties using a local neighborhood in a spatial prior model. The Leading Edge.

Martin Jullum, Odd Kolbjørnsen (2016). A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties. Geophysics.

Gudmund Horn Hermansen, Nils Lid Hjort, Martin Jullum (2015). Parametric or nonparametric: The FIC approach for stationary time series. Proceedings of the 60th World Statistics Congress of the International Statistical Institute.

Martin Jullum, Odd Kolbjørnsen (2015). An approximate Bayesian inversion framework based on local-Gaussian likelihoods. Petroleum Geostatistics 2015.

Martin Jullum (2012). Focused Information criteria for selecting among parametric and nonparametric models.