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Martin Jullum
Martin Jullum
Norwegian Computing Center
Verified email at nr.no - Homepage
Title
Cited by
Cited by
Year
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
K Aas, M Jullum, A Løland
Artificial Intelligence, 2019
5372019
Detecting money laundering transactions with machine learning
M Jullum, A Løland, RB Huseby, G Ånonsen, J Lorentzen
Journal of Money Laundering Control 23 (1), 173-186, 2020
1422020
A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties
M Jullum, O Kolbjørnsen
Geophysics 81 (3), R75-R87, 2016
512016
Parametric or nonparametric: The FIC approach
M Jullum, NL Hjort
Statistica Sinica, 951-981, 2017
332017
What price semiparametric Cox regression?
M Jullum, NL Hjort
Lifetime data analysis 25 (3), 406-438, 2019
222019
Bayesian AVO inversion to rock properties using a local neighborhood in a spatial prior model
O Kolbj⊘ rnsen, A Buland, R Hauge, P R⊘ e, M Jullum, RW Metcalfe, ...
The Leading Edge 35 (5), 431-436, 2016
222016
shapr: An R-package for explaining machine learning models with dependence-aware Shapley values
N Sellereite, M Jullum
Journal of Open Source Software, 2020
212020
Explaining predictive models using Shapley values and non-parametric vine copulas
K Aas, T Nagler, M Jullum, A Løland
Dependence modeling 9 (1), 62-81, 2021
192021
Explaining predictive models with mixed features using Shapley values and conditional inference trees
A Redelmeier, M Jullum, K Aas
Machine Learning and Knowledge Extraction: 4th IFIP TC 5, TC 12, WG 8.4, WG …, 2020
192020
Comparison of contextual importance and utility with lime and Shapley values
K Främling, M Westberg, M Jullum, M Madhikermi, A Malhi
International Workshop on Explainable, Transparent Autonomous Agents and …, 2021
172021
Using Shapley values and variational autoencoders to explain predictive models with dependent mixed features
LHB Olsen, IK Glad, M Jullum, K Aas
Journal of machine learning research 23 (213), 1-51, 2022
142022
groupShapley: Efficient prediction explanation with Shapley values for feature groups
M Jullum, A Redelmeier, K Aas
arXiv preprint arXiv:2106.12228, 2021
112021
Pairwise local Fisher and naive Bayes: Improving two standard discriminants
H Otneim, M Jullum, D Tjøstheim
Journal of econometrics 216 (1), 284-304, 2020
72020
Efficient and simple prediction explanations with groupShapley: A practical perspective
M Jullum, A Redelmeier, K Aas
2nd Italian Workshop on Explainable Artificial Intelligence 3014, 28-43, 2021
52021
MCCE: Monte Carlo sampling of realistic counterfactual explanations
A Redelmeier, M Jullum, K Aas, A Løland
arXiv preprint arXiv:2111.09790, 2021
42021
Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling
M Jullum, T Thorarinsdottir, FE Bachl
Journal of the Royal Statistical Society: Series C (Applied Statistics) 69 …, 2020
42020
Investigating mesh‐based approximation methods for the normalization constant in the log Gaussian Cox process likelihood
M Jullum
Stat 9 (1), e285, 2020
42020
A comparative study of methods for estimating conditional Shapley values and when to use them
LHB Olsen, IK Glad, M Jullum, K Aas
arXiv preprint arXiv:2305.09536, 2023
32023
Parametric or nonparametric: the FIC approach for stationary time series
GH Hermansen, NL Hjort, M Jullum
Proceedings of the 60th World Statistics Congress of the International …, 2015
32015
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values (2020)
K Aas, M Jullum, A Løland
URL: http://arxiv. org/abs, 1903
31903
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