Martin Jullum

Introduction to Local Model-Agnostic methods in XAI 2025. Lecture

Improving the weighting strategy in KernelSHAP 2025. Poster

Hva skjer når avfall møter algoritmer? , 2025. Media participation

AI/ML for 5G and Beyond Cybersecurity arXiv.org, (ISSN 2331-8422 ), doi: https://doi.org/10.48550/arXiv.2505.18402 , 2025. Scientific article

MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data Data mining and knowledge discovery, pp. 1830 1861 , (ISSN 1384-5810 1573-756X ), doi: https://doi.org/10.1007/s10618-024-01017-y , 2024. Scientific article

A comparative study of methods for estimating model-agnostic Shapley value explanations Data mining and knowledge discovery, vol. 38, pp. 1782 1829 , (ISSN 1384-5810 1573-756X ), doi: https://doi.org/10.1007/s10618-024-01016-z , 2024. Scientific article

Statistiske metoder versus maskinlæringsmetoder 2024. Lecture

Hvorfor er maskinlæring nødvendig i kampen mot hvitvasking? , 2024. Media participation

Recent computational advances in Shapley values based prediction explanation 2024. Scientific lecture

On conditional Shapley values for prediction explanation - Adaptive & variance stabilizing estimation with KernelSHAP 2024. Poster

Introduction to the 1st Oslo Invitational Workshop on Model-Agnostic Explainable AI 2024. Lecture

Forskning på ML for hvitvaskingsdeteksjon 2024. Lecture

eXplego: An XAI-method selection tool 2024. Lecture

How to navigate in the Explainable AI jungle 2024. Lecture

Hvordan forklarer vi kunstig intelligens 2024. Lecture

Introduction to XAI 2024. Lecture

More effective computation of Shapley values 2024. Scientific lecture

Introduction to XAI 2024. Lecture

Olav Nikolai Risdal Breivik; Martin Jullum; Leveraging Norwegian Data to Improve Danish Insurance Risk Models NR-notat, 2024. Scientific article

Leveraging Norwegian Data to Improve Danish Insurance Risk Models 2024. Report

Finetuning credit scoring ensemble models for FundingPartner 2024. Report

Statistical Embedding: Beyond Principal Components Statistical Science, vol. 38, pp. 411 439 , (ISSN 0883-4237 2168-8745 ), doi: https://doi.org/10.1214/22-STS881 , 2023. Scientific article

Introduksjon til sentrale metoder i statistisk modellering og maskinlæring 2023. Lecture

Forsker tror ikke strømregningen blir mye lavere med ny støtteordning 2023. Media interview

Et forslag til strømstøtte basert på timespriser , 2023. Article feature

Some recent trends in embeddings of time series and dynamic networks Journal of Time Series Analysis, vol. 44, pp. 686 709 , (ISSN 0143-9782 1467-9892 ), doi: https://doi.org/10.1111/jtsa.12677 , 2023. Scientific article

A ridiculously simple approach to counterfactual explanations 2023. Scientific lecture

eXplego: An interactive Tool that Helps you Select Appropriate XAI-methods for your Explainability Needs CEUR Workshop Proceedings, vol. 3554, pp. 146 151 , (ISSN 1613-0073 ), , 2023. Scientific article

A ridiculously simple approach to counterfactual explanations 2023. Scientific lecture

ML in AML - Machine Learning for Anti Money Laundering 2023. Lecture

Deteksjon av hvitvasking 2023. Lecture

Why AI needs maths and stats -- lessons from working in a CS field 2023. Lecture

Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features Journal of machine learning research, vol. 23, pp. 1 51 , (ISSN 1532-4435 1533-7928 ), , 2022. Scientific article

Saldoprognoser 2022. Report

AI/ML for 5G and Beyond Cybersecurity 2022. Report

Statistical embedding: Beyond principal components 2022. Lecture

Prediction Explanation with Shapley values 2022. Scientific lecture

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values Artificial Intelligence, vol. 298, (ISSN 0004-3702 1872-7921 ), doi: https://doi.org/10.1016/j.artint.2021.103502 , 2021. Scientific article

groupSHAP: Efficient Shapley value explanation through feature groups 2021. Poster

Efficient and simple prediction explanations with groupShapley: A practical perspective 2021. Scientific lecture

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values 2021. Scientific lecture

Efficient Shapley value explanations through feature groups 2021. Scientific lecture

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values 2021. Poster

groupShapley: Efficient prediction explanation with Shapley values for feature groups , 2021. Report

Comparison of Contextual Importance and Utility with LIME and Shapley Values Lecture Notes in Computer Science (LNCS), vol. 12688, pp. 39 54 , (ISSN 0302-9743 1611-3349 ), doi: https://doi.org/10.1007/978-3-030-82017-6_3 , 2021. Scientific article

Efficient and simple prediction explanations with groupShapley: A practical perspective CEUR Workshop Proceedings, vol. 3014, (ISSN 1613-0073 ), , 2021. Scientific article

Explaining predictive models using Shapley values and non-parametric vine copulas Dependence Modeling, vol. 9, pp. 62 81 , (ISSN 2300-2298 ), doi: https://doi.org/10.1515/demo-2021-0103 , 2021. Scientific article

White paper on performance evaluation of volatility estimation methods for Exabel 2021. Report

Whitepaper on Exabel’s Factor Model 2021. Report

How to open the black box – individual prediction explanation 2020. Scientific lecture

Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees pp. 117 137 , doi: https://doi.org/10.1007/978-3-030-57321-8_7 , 2020. Scientific chapter / article / conference article

Investigating mesh-based approximation methods for the normalization constant in the log Gaussian Cox process likelihood Stat, vol. 9, (ISSN 2049-1573 ), doi: https://doi.org/10.1002/sta4.285 , 2020. Scientific article

shapr: An R-package for explaining machine learning models with dependence-aware Shapley values Journal of Open Source Software (JOSS), vol. 5, (ISSN 2475-9066 ), doi: https://doi.org/10.21105/joss.02027 , 2020. Scientific article

Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling Journal of the Royal Statistical Society, Series C (Applied Statistics), vol. 69, (ISSN 0035-9254 1467-9876 ), doi: https://doi.org/10.1111/rssc.12397 , 2020. Scientific article

Pairwise local Fisher and naive Bayes: Improving two standard discriminants Journal of Econometrics, vol. 216, pp. 284 304 , (ISSN 0304-4076 1872-6895 ), doi: https://doi.org/10.1016/j.jeconom.2020.01.019 , 2020. Scientific article

Detecting money laundering transactions with machine learning Journal of Money Laundering Control, vol. 23, pp. 173 186 , (ISSN 1368-5201 1758-7808 ), doi: https://doi.org/10.1108/JMLC-07-2019-0055 , 2020. Scientific article

Opening the black box -- individual prediction explanation 2019. Scientific lecture

How to open the black box -- Individual prediction explanation 2019. Scientific lecture

Mindre rutinearbeid med maskinlæring -- Automatisk deteksjon av hvitvasking 2019. Lecture

Kjersti Aas; Martin Jullum; Anders Løland; Shapley explanations using conditional inference trees 2019. Report

XGBoost - efficient tree boosting 2018. Lecture

Martin Jullum; Nils Lid Hjort; What price semiparametric Cox regression? Lifetime Data Analysis, pp. 406 438 , doi: https://doi.org/10.1007/s10985-018-9450-7 , 2018. Scientific article

Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling , 2018. Report

Detecting money laundering transactions -- which transactions should we learn from? 2018. Report

Detecting money laundering transactions – two stories 2018. Lecture

Parametric or nonparametric, that’s the question 2018. Scientific lecture

Martin Jullum; Nils Lid Hjort; Parametric or nonparametric: The FIC approach Statistica sinica, pp. 951 981 , doi: https://doi.org/10.5705/ss.202015.0364 , 2017. Scientific article

Maskinlæring for vurdering av forsikringsrisiko 2017. Report

Bayesian modelling of cluster point process models 2017. Scientific lecture

A focused model selection criterion for selecting among parametric and nonparametric models 2017. Scientific lecture

Estimating the seal pup abundance in the Greenland Sea with Bayesian hierarchical modeling 2017. Scientific lecture

Statistical modeling of repertoire overlap in entire sampling spaces , 2017. Report

Empirical likelihood 2016. Lecture

Estimating seal pup abundance with LGCP 2016. Scientific lecture

FIC with a nonparametric candidate – a new strategy for FIC construction 2016. Scientific lecture

New focused approaches to topics within model selection and approximate Bayesian inversion 2016. Doctor dissertat

Bayesian AVO inversion to rock properties using a local neighborhood in a spatial prior model The Leading Edge, vol. 35, pp. 431 436 , (ISSN 1070-485X 1938-3789 ), doi: https://doi.org/10.1190/tle35050431.1 , 2016. Scientific article

New focused approaches to topics within model selection and approximate Bayesian inversion 2016. Lecture

Martin Jullum; Odd Kolbjørnsen; A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties Geophysics, pp. R75 R87 , doi: https://doi.org/10.1190/geo2015-0314.1 , 2016. Scientific article

Nils Lid Hjort; Martin Jullum; To liv: kvinnene i Lillestrøm som ble født på samme dag og døde på samme dag , 2015. Website material

Nils Lid Hjort; To liv: kvinnene i Lillestrøm som ble født på samme dag og døde på samme dag (FocuStat Blog Post) , 2015. Website material

A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties Geophysics, vol. 81, pp. R75 R87 , (ISSN 0016-8033 1942-2156 ), doi: https://doi.org/10.1190/GEO2015-0314.1 , 2015. Scientific article

Gudmund Horn Hermansen; Nils Lid Hjort; Martin Jullum; Parametric or nonparametric: the FIC approach for stationary time series pp. 4827 4832 , , 2015. Scientific chapter / article / conference article

Hvis man sjekker en billion kvinner, vil man finne fem slike par , 2015. Media participation

Martin Jullum; An approximate Bayesian geophysical inversion framework based on local-Gaussian likelihoods 2015. Scientific lecture

Martin Jullum; Parametric of Nonparametric: The FIC Approach for Time Series , 2015. Scientific lecture

An Approximate Bayesian Inversion Framework based on Local-Gaussian Likelihoods EarthDoc, doi: https://doi.org/10.3997/2214-4609.201413634 , 2015. Abstract

An Approximate Bayesian Inversion Framework based on Local-gaussian Likelihoods 2015. Scientific lecture

Martin Jullum; Nils Lid Hjort; Parametric or Nonparametric: The Focused Information Criterion Approach 2014. Scientific lecture

Parametric or Nonparametric: The Focused Information Criterion Approach (+ Approximate Bayesian Inference) 2013. Scientific lecture

Martin Jullum; Approximate Bayesian Inference for Geophysical Inverse Problems 2013. Lecture

Martin Jullum; Nils Lid Hjort; Parametric or Nonparametric: The FIC Approach 2013. Scientific lecture

Martin Jullum; Nils Lid Hjort; Parametric or Nonparametric: The FIC Approach 2013. Scientific lecture

Martin Jullum; Focused information criteria for selecting among parametric and nonparametric models , 2012. Thesis master

Vindprognoser og strømpriser 2011. Report

Strekker Laban seg litt lengre? Forskning.no, (ISSN 1891-635X 1891-6341 ), , 2011. Science for the public article

Hypotesetesting av strekklengde for Seigmenn 2011. Report

Kary Främling; Marcus Westberg; Martin Jullum; Manik Madhikermi; Avleen Kaur Malhi; Comparison of Contextual Importance and Utility with LIME and Shapley Values doi: https://doi.org/10.1007/978-3-030-82017-6_3 , 2009. Scientific chapter / article / conference article