Martin Jullum

Martin Jullum; Frida Svendal Aase; Kjersti Aas; More effective computation of Shapley values 2024. Scientific lecture

Annabelle Alice Redelmeier; Martin Jullum; Kjersti Aas; Anders Løland; 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

Lars Henry Berge Olsen; Ingrid Kristine Glad; Martin Jullum; Kjersti Aas; 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

Martin Jullum; Kjersti Aas; Statistiske metoder versus maskinlæringsmetoder 2024. Lecture

Hvorfor er maskinlæring nødvendig i kampen mot hvitvasking? , 2024. Popular science hosting

Martin Jullum; Kjersti Aas; Frida Svendal Aase; Anders Løland; Recent computational advances in Shapley values based prediction explanation 2024. Scientific lecture

Martin Jullum; Kjersti Aas; Anders Løland; Frida Svendal Aase; Lars Henry Berge Olsen; On conditional Shapley values for prediction explanation - Adaptive & variance stabilizing estimation with KernelSHAP 2024. Poster

Martin Jullum; Kjersti Aas; Anders Løland; Introduction to the 1st Oslo Invitational Workshop on Model-Agnostic Explainable AI 2024. Lecture

Martin Jullum; Forskning på ML for hvitvaskingsdeteksjon 2024. Lecture

Martin Jullum; Anders Løland; Robindra Prabhu; Jacob Sjødin; eXplego: An XAI-method selection tool 2024. Lecture

Martin Jullum; How to navigate in the Explainable AI jungle 2024. Lecture

Martin Jullum; Hvordan forklarer vi kunstig intelligens 2024. Lecture

Martin Jullum; Introduction to XAI 2024. Lecture

Martin Jullum; Kjersti Aas; Anders Løland; Annabelle Alice Redelmeier; A ridiculously simple approach to counterfactual explanations 2023. Scientific lecture

Dag Bjarne Tjøstheim; Martin Jullum; Anders Løland; Some recent trends in embeddings of time series and dynamic networks Journal of Time Series Analysis, vol. 44, pp. 686 709 0 , (ISSN 0143-9782 1467-9892 ), doi: https://doi.org/10.1111/jtsa.12677 , 2023. Scientific article

Solveig Engebretsen; Martin Jullum; Anders Løland; Introduksjon til sentrale metoder i statistisk modellering og maskinlæring 2023. Lecture

Dag Bjarne Tjøstheim; Martin Jullum; Anders Løland; Statistical Embedding: Beyond Principal Components Statistical Science, vol. 38, pp. 411 439 28 , (ISSN 0883-4237 2168-8745 ), doi: https://doi.org/10.1214/22-STS881 , 2023. Scientific article

Martin Jullum; Forsker tror ikke strømregningen blir mye lavere med ny støtteordning Intervju Dagens Næringsliv, 2023. Interview

Martin Jullum; Et forslag til strømstøtte basert på timespriser Dagens næringsliv, (ISSN 0803-9372 ), 2023. Article feature

Martin Jullum; Jacob Sjødin; Robindra Prabhu; Anders Løland; 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 1613-0073 ), , 2023. Scientific article

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

Martin Jullum; Deteksjon av hvitvasking 2023. Lecture

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

Martin Jullum; Kjersti Aas; Anders Løland; Annabelle Alice Redelmeier; A ridiculously simple approach to counterfactual explanations 2023. Scientific lecture

Martin Jullum; Prediction Explanation with Shapley values 2022. Scientific lecture

Hanne Therese Wist Rognebakke; Kjersti Aas; Martin Jullum; Saldoprognoser 2022. Report

Sandeep Pirbhulal; Habtamu Abie; Martin Jullum; Didrik Nielsen; Anders Løland; AI/ML for 5G and Beyond Cybersecurity 2022. Report

Dag Bjarne Tjøstheim; Martin Jullum; Anders Løland; Statistical embedding: Beyond principal components 2022. Lecture

Lars Henry Berge Olsen; Ingrid Kristine Glad; Martin Jullum; Kjersti Aas; Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features Journal of machine learning research, vol. 23, pp. 1 51 50 , (ISSN 1532-4435 1533-7928 ), , 2022. Scientific article

Martin Jullum; Annabelle Alice Redelmeier; Kjersti Aas; Efficient and simple prediction explanations with groupShapley: A practical perspective 2021. Scientific lecture

Martin Jullum; Kjersti Aas; Anders Løland; Explaining individual predictions when features are dependent: More accurate approximations to Shapley values 2021. Scientific lecture

Martin Jullum; Annabelle Alice Redelmeier; Kjersti Aas; Efficient Shapley value explanations through feature groups 2021. Scientific lecture

Martin Jullum; Annabelle Alice Redelmeier; Kjersti Aas; groupShapley: Efficient prediction explanation with Shapley values for feature groups 2021. Report

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

Kjersti Aas; Martin Jullum; Anders Løland; Explaining individual predictions when features are dependent: More accurate approximations to Shapley values 2021. Poster

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

Kjersti Aas; Martin Jullum; Anders Løland; Explaining individual predictions when features are dependent: More accurate approximations to Shapley values Artificial Intelligence, vol. 298, pp. 24 , (ISSN 0004-3702 1872-7921 ), doi: https://doi.org/10.1016/j.artint.2021.103502 , 2021. Scientific article

Øyvind Grotmol; Michael Scheuerer; Kjersti Aas; Martin Jullum; Whitepaper on Exabel’s Factor Model 2021. Report

Øyvind Grotmol; Martin Jullum; Kjersti Aas; Michael Scheuerer; White paper on performance evaluation of volatility estimation methods for Exabel 2021. Report

Kary Främling; Marcus Westberg; Martin Jullum; Manik Madhikermi; Avleen Kaur Malhi; 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

Martin Jullum; Annabelle Alice Redelmeier; Kjersti Aas; Efficient and simple prediction explanations with groupShapley: A practical perspective CEUR Workshop Proceedings, vol. 3014, pp. 15 , (ISSN 1613-0073 1613-0073 ), , 2021. Scientific article

Martin Jullum; Anders Løland; Ragnar Bang Huseby; Geir Ånonsen; Johannes P Lorentzen; 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

Håkon Otneim; Martin Jullum; Dag Bjarne Tjøstheim; Pairwise local Fisher and naive Bayes: Improving two standard discriminants Journal of Econometrics, vol. 216, pp. 284 304 21 , (ISSN 0304-4076 1872-6895 ), doi: https://doi.org/10.1016/j.jeconom.2020.01.019 , 2020. Scientific article

Annabelle Alice Redelmeier; Martin Jullum; Kjersti Aas; 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

Martin Jullum; Investigating mesh-based approximation methods for the normalization constant in the log Gaussian Cox process likelihood Stat, vol. 9, pp. 0 , (ISSN 2049-1573 2049-1573 ), doi: https://doi.org/10.1002/sta4.285 , 2020. Scientific article

Nikolai Sellereite; Martin Jullum; 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 2475-9066 ), doi: https://doi.org/10.21105/joss.02027 , 2020. Scientific article

Martin Jullum; Thordis Linda Thorarinsdottir; Fabian E. Bachl; Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling The Journal of the Royal Statistical Society, Series C (Applied Statistics), vol. 69, pp. 0 , (ISSN 0035-9254 1467-9876 ), doi: https://doi.org/10.1111/rssc.12397 , 2020. Scientific article

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

Martin Jullum; Kjersti Aas; Anders Løland; How to open the black box -- Individual prediction explanation 2019. Scientific lecture

Martin Jullum; Kjersti Aas; Anders Løland; Opening the black box -- individual prediction explanation 2019. Scientific lecture

Martin Jullum; Lars Erik Bolstad; Mindre rutinearbeid med maskinlæring -- Automatisk deteksjon av hvitvasking 2019. Lecture

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

Martin Jullum; XGBoost - efficient tree boosting 2018. Lecture

Martin Jullum; Anders Løland; Ragnar Bang Huseby; Geir Ånonsen; Johannes P Lorentzen; Detecting money laundering transactions -- which transactions should we learn from? 2018. Report

Anders Løland; Martin Jullum; Ragnar Bang Huseby; Detecting money laundering transactions – two stories 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

Martin Jullum; Thordis Linda Thorarinsdottir; Fabian Bachl; Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling , 2018. Report

Martin Jullum; Nils Lid Hjort; 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

Kjersti Aas; Martin Jullum; Linda Reiersølmoen Neef; Maskinlæring for vurdering av forsikringsrisiko 2017. Report

Lars Holden; Martin Jullum; Geir Kjetil Sandve; Statistical modeling of repertoire overlap in entire sampling spaces , 2017. Report

Thordis Linda Thorarinsdottir; Martin Jullum; Peter Guttorp; Bayesian modelling of cluster point process models 2017. Scientific lecture

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

Martin Jullum; Thordis Linda Thorarinsdottir; Fabian Bachl; Estimating the seal pup abundance in the Greenland Sea with Bayesian hierarchical modeling 2017. Scientific lecture

Martin Jullum; Thordis Linda Thorarinsdottir; Fabian Bachl; Estimating seal pup abundance with LGCP 2016. Scientific lecture

Martin Jullum; Empirical likelihood 2016. Lecture

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

Odd Kolbjørnsen; Arild Buland; Ragnar Hauge; Per Røe; Martin Jullum; et al. 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

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

Martin Jullum; FIC with a nonparametric candidate – a new strategy for FIC construction 2016. Scientific 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

Martin Jullum; Odd Kolbjørnsen; An Approximate Bayesian Inversion Framework based on Local-Gaussian Likelihoods EarthDoc, pp. 4 , doi: https://doi.org/10.3997/2214-4609.201413634 , 2015. Abstract

Martin Jullum; Odd Kolbjørnsen; An Approximate Bayesian Inversion Framework based on Local-gaussian Likelihoods 2015. Scientific lecture

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

Martin Jullum; Odd Kolbjørnsen; 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

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

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

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

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

Martin Jullum; Nils Lid Hjort; Parametric or Nonparametric: The FIC Approach 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; Focused information criteria for selecting among parametric and nonparametric models , 2012. Thesis master

Martin Jullum; Hypotesetesting av strekklengde for Seigmenn 2011. Report

Martin Jullum; Vindprognoser og strømpriser 2011. Report

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

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