| Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K Aas, M Jullum, A Løland Artificial Intelligence, 2019 | 806 | 2019 |
| 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 | 204 | 2020 |
| A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties M Jullum, O Kolbjørnsen Geophysics 81 (3), R75-R87, 2016 | 58 | 2016 |
| Parametric or nonparametric: The FIC approach M Jullum, NL Hjort Statistica Sinica, 951-981, 2017 | 36 | 2017 |
| 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 | 31 | 2020 |
| 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 | 27 | 2022 |
| 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 | 26 | 2021 |
| shapr: An R-package for explaining machine learning models with dependence-aware Shapley values N Sellereite, M Jullum Journal of Open Source Software, 2020 | 25 | 2020 |
| What price semiparametric Cox regression? M Jullum, NL Hjort Lifetime data analysis 25 (3), 406-438, 2019 | 25 | 2019 |
| groupShapley: Efficient prediction explanation with Shapley values for feature groups M Jullum, A Redelmeier, K Aas arXiv preprint arXiv:2106.12228, 2021 | 23 | 2021 |
| 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 | 23 | 2021 |
| 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 | 23 | 2016 |
| A comparative study of methods for estimating model-agnostic Shapley value explanations LHB Olsen, IK Glad, M Jullum, K Aas Data Mining and Knowledge Discovery 38 (4), 1782-1829, 2024 | 19* | 2024 |
| MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data A Redelmeier, M Jullum, K Aas, A Løland Data Mining and Knowledge Discovery 38 (4), 1830-1861, 2024 | 15* | 2024 |
| 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 | 9 | 2021 |
| Explaining individual predictions when features are dependent: More accurate approximations to shapley values (2019) K Aas, M Jullum, A Løland arXiv preprint arXiv:1903.10464, 1903 | 8 | 1903 |
| Finding money launderers using heterogeneous graph neural networks F Johannessen, M Jullum arXiv preprint arXiv:2307.13499, 2023 | 7 | 2023 |
| Pairwise local Fisher and naive Bayes: Improving two standard discriminants H Otneim, M Jullum, D Tjøstheim Journal of Econometrics 216 (1), 284-304, 2020 | 7 | 2020 |
| Some recent trends in embeddings of time series and dynamic networks D Tjøstheim, M Jullum, A Løland Journal of Time Series Analysis 44 (5-6), 686-709, 2023 | 6 | 2023 |
| 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 | 5 | 2020 |
Kjersti AasAssistant research director Norsk RegnesentralPotvrđena adresa e-pošte na nr.no