Publication profile
Publication profile
Martin Jullum
Name:
Martin Jullum
Title:
Seniorforsker / Senior Research Scientist
Phone:
(+47) +47 22 85 26 08 Mob: +47 476 33 245
Email:
Jullum [at] nr [dot] no
Scientific areas:
Statistical analysis and methodology, Machine learning, Model selection
Add to contacts (vCard) Show publications
Academic article
2022
. Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features. Journal of machine learning research (ISSN 1532-4435). 23(213) pp 1-51. 2022. Fulltekst
2021
. Efficient and simple prediction explanations with groupShapley: A practical perspective. CEUR Workshop Proceedings (ISSN 1613-0073). 3014 2021. Fulltekst
. Comparison of Contextual Importance and Utility with LIME and Shapley Values. Lecture Notes in Computer Science (LNCS) (ISSN 0302-9743). 12688 pp 39-54. doi: 10.1007/978-3-030-82017-6_3. 2021.
. Explaining predictive models using Shapley values and non-parametric vine copulas. Dependence Modeling (ISSN 2300-2298). 9(1) pp 62-81. doi: 10.1515/demo-2021-0103. 2021. Fulltekst
. Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. Artificial Intelligence (ISSN 0004-3702). 298 doi: 10.1016/j.artint.2021.103502. 2021.
2020
. shapr: An R-package for explaining machine learning models with dependence-aware Shapley values. Journal of Open Source Software (JOSS) (ISSN 2475-9066). 5(46) doi: 10.21105/joss.02027. 2020. Arkiv
. Investigating mesh-based approximation methods for the normalization constant in the log Gaussian Cox process likelihood. Stat (ISSN 2049-1573). 9(1) doi: 10.1002/sta4.285. 2020.
. Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling. The Journal of the Royal Statistical Society, Series C (Applied Statistics) (ISSN 0035-9254). 69(2) doi: 10.1111/rssc.12397. 2020.
. Detecting money laundering transactions with machine learning. Journal of Money Laundering Control (ISSN 1368-5201). 23(1) pp 173-186. doi: 10.1108/JMLC-07-2019-0055. 2020. Fulltekst
. Pairwise local Fisher and naive Bayes: Improving two standard discriminants. Journal of Econometrics (ISSN 0304-4076). 216(1) pp 284-304. doi: 10.1016/j.jeconom.2020.01.019. 2020. Arkiv
2018
. What price semiparametric Cox regression? Lifetime Data Analysis (ISSN 1380-7870). 25(3) pp 406-438. doi: 10.1007/s10985-018-9450-7. 2018. Fulltekst Arkiv
2017
. Parametric or nonparametric: The FIC approach. Statistica sinica (ISSN 1017-0405). 27(3) pp 951-981. doi: 10.5705/ss.202015.0364. 2017.
2016
. Bayesian AVO inversion to rock properties using a local neighborhood in a spatial prior model. The Leading Edge (ISSN 1070-485X). 35(5) pp 431-436. doi: 10.1190/tle35050431.1. 2016.
. A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties. Geophysics (ISSN 0016-8033). 81(3) pp R75-R87. doi: 10.1190/geo2015-0314.1. 2016.
2015
. A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties. Geophysics (ISSN 0016-8033). 81(3) pp R75-R87. doi: 10.1190/GEO2015-0314.1. 2015.
Academic chapter/article/Conference paper
2020
. Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees. In: Lecture Notes in Computer Science (LNCS). (ISBN 978-3-030-58805-2). pp 117-137. doi: 10.1007/978-3-030-57321-8_7. 2020. Arkiv
2015
. Parametric or nonparametric: the FIC approach for stationary time series. In: Proceedings of the 60th World Statistics Congress of the International Statistical Institute, ISI2015. (ISBN 978-90-73592-35-3). pp 4827-4832. 2015. Fulltekst
Academic lecture
2022
. Prediction Explanation with Shapley values. Explainable AI Seminars @ Imperial; Online, 2/3/2022.
2021
. Efficient Shapley value explanations through feature groups. The 28th Nordic Conference in Mathematical Statistics; Tromsø, Norway/ Online, 6/21/2021 - 6/24/2021.
. Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. 30th International Joint Conference on Artificial Intelligence; Online/Montreal, 8/19/2021 - 8/26/2021.
. Efficient and simple prediction explanations with groupShapley: A practical perspective. Italian Workshop on Explainable Artificial Intelligence 2021; Milano, Italy/ Online, 12/1/2021 - 12/3/2021.
2020
. How to open the black box – individual prediction explanation. Statistics seminar series at Department of Mathematical Sciences, NTNU; Trondheim, 3/9/2020.
2019
. Opening the black box -- individual prediction explanation. Big Insight Day 2020; Oslo, 11/14/2019.
. How to open the black box -- Individual prediction explanation. Det 20. norske statistikermøtet; Stavanger, 6/18/2019 - 6/20/2019.
2018
. Parametric or nonparametric, that’s the question. The FocuStat conference 2018, 5/22/2018 - 5/25/2018.
2017
. Estimating the seal pup abundance in the Greenland Sea with Bayesian hierarchical modeling. Det 17. norske statistikermøtet; Fredrikstad, 6/12/2017 - 6/15/2017.
. A focused model selection criterion for selecting among parametric and nonparametric models. Building Bridges at Bislett, 5/22/2017 - 5/24/2017.
. Bayesian modelling of cluster point process models. Spatial Statistics 2017; Lancaster, 7/4/2017 - 7/7/2017.
2016
. Estimating seal pup abundance with LGCP. Autumn Meeting on Latent Gaussian Models, 10/10/2016 - 10/11/2016.
. FIC with a nonparametric candidate – a new strategy for FIC construction. FICology; Oslo, 5/9/2016 - 5/11/2016.
2015
. An Approximate Bayesian Inversion Framework based on Local-gaussian Likelihoods. Petroleum Geostatistics; Biarritz, 9/7/2015 - 9/11/2015.
. An approximate Bayesian geophysical inversion framework based on local-Gaussian likelihoods. Oslo Graduate School in Biostatistics Workshop; Klækken, Hønefoss, 5/29/2015 - 5/30/2015.
. Parametric of Nonparametric: The FIC Approach for Time Series. 18th Norwegian Statistical Meeting; Solstrand, Bergen, 6/16/2015 - 6/18/2015. Omtale
2014
. Parametric or Nonparametric: The Focused Information Criterion Approach. 2014 Joint Statistical Meetings; Boston, 8/2/2014 - 8/7/2014.
2013
. Parametric or Nonparametric: The Focused Information Criterion Approach (+ Approximate Bayesian Inference). SFI-lunsj; Norsk Regnesentral, Oslo, 10/20/2013.
. Parametric or Nonparametric: The FIC Approach. 29-th European Meeting of Statisticians; Budapest, 7/20/2013 - 7/25/2103 1:00:00 AM.
. Parametric or Nonparametric: The FIC Approach. 17th Norwegian Statistical meeting; Halden, 6/11/2013 - 6/13/2013.
Abstract
2015
. An Approximate Bayesian Inversion Framework based on Local-Gaussian Likelihoods. EarthDoc. doi: 10.3997/2214-4609.201413634. 2015. Omtale
Lecture
2019
. Mindre rutinearbeid med maskinlæring -- Automatisk deteksjon av hvitvasking. Make Data Smart Again; Oslo, 5/9/2019.
2018
. XGBoost - efficient tree boosting. Big Insight lunch seminar, 1/31/2018.
. Detecting money laundering transactions – two stories. DNB Data Summit 2018; Oslo, 11/30/2018.
2016
. New focused approaches to topics within model selection and approximate Bayesian inversion. Disputas, 4/1/2016.
. Empirical likelihood. Trial lecture, PhD, 4/1/2016.
2013
. Approximate Bayesian Inference for Geophysical Inverse Problems. Oslo Graduate School in Biostatistics Workshop; Hønefoss, 5/24/2013 - 5/25/2013.
Poster
2021
. Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. The 30th International Joint Conference on Artificial Intelligence (IJCAI-21); Montreal (virtuelt), 8/19/2021 - 8/26/2021.
. groupSHAP: Efficient Shapley value explanation through feature groups. Geilo Winter School; Online, 1/25/2021 - 3/29/2021.
Doctoral dissertation
2016
. New focused approaches to topics within model selection and approximate Bayesian inversion. Faculty of Mathematics and Natural Sciences, University of Oslo. pp 185. 2016.
Masters thesis
2012
. Focused information criteria for selecting among parametric and nonparametric models.. 2012. Omtale
Report
2021
. groupShapley: Efficient prediction explanation with Shapley values for feature groups. Norsk Regnesentral. NR-notat SAMBA/20/21. 2021.
. Whitepaper on Exabel’s Factor Model. EXABEL. pp 7. 2021.
. White paper on performance evaluation of volatility estimation methods for Exabel. EXABEL. pp 12. 2021.
2019
. Shapley explanations using conditional inference trees. Norsk Regnesentral. NR-notat SAMBA/18/19. pp 33. 2019.
2018
. Detecting money laundering transactions -- which transactions should we learn from? Norsk Regnesentral. NR-notat SAMBA/06/18. pp 22. 2018.
. Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling. Norsk Regnesentral. NR-notat SAMBA/04/18. pp 25. 2018. Fulltekst
2017
. Statistical modeling of repertoire overlap in entire sampling spaces. Norsk Regnesentral. NR-notat SAMBA/13/2017. pp 15. 2017. Fulltekst
. Maskinlæring for vurdering av forsikringsrisiko. Norsk Regnesentral. NR-notat SAMBA/14/2017. pp 81. 2017.
2011
. Hypotesetesting av strekklengde for Seigmenn. Norsk Regnesentral. NR-notat SAMBA/25/11. pp 28. 2011.
. Vindprognoser og strømpriser. Norsk Regnesentral. NR-notat SAMBA/26/11. pp 21. 2011.
Popular scientific article
2011
Programme participation
2015
. Hvis man sjekker en billion kvinner, vil man finne fem slike par. 2015. Aftenposten TV [TV] 10/22/2015. Fulltekst
Website (informational material)
2015
. To liv: kvinnene i Lillestrøm som ble født på samme dag og døde på samme dag (FocuStat Blog Post). 2015. Fulltekst
| Name: | Martin Jullum |
| Title: | Seniorforsker / Senior Research Scientist |
| Phone: | (+47) +47 22 85 26 08 Mob: +47 476 33 245 |
| Email: | Jullum [at] nr [dot] no |
| Scientific areas: | Statistical analysis and methodology, Machine learning, Model selection |
| Add to contacts (vCard) |
| Show publications |
Academic article
2022
. Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features. Journal of machine learning research (ISSN 1532-4435). 23(213) pp 1-51. 2022. Fulltekst
2021
. Efficient and simple prediction explanations with groupShapley: A practical perspective. CEUR Workshop Proceedings (ISSN 1613-0073). 3014 2021. Fulltekst
. Comparison of Contextual Importance and Utility with LIME and Shapley Values. Lecture Notes in Computer Science (LNCS) (ISSN 0302-9743). 12688 pp 39-54. doi: 10.1007/978-3-030-82017-6_3. 2021.
. Explaining predictive models using Shapley values and non-parametric vine copulas. Dependence Modeling (ISSN 2300-2298). 9(1) pp 62-81. doi: 10.1515/demo-2021-0103. 2021. Fulltekst
. Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. Artificial Intelligence (ISSN 0004-3702). 298 doi: 10.1016/j.artint.2021.103502. 2021.
2020
. shapr: An R-package for explaining machine learning models with dependence-aware Shapley values. Journal of Open Source Software (JOSS) (ISSN 2475-9066). 5(46) doi: 10.21105/joss.02027. 2020. Arkiv
. Investigating mesh-based approximation methods for the normalization constant in the log Gaussian Cox process likelihood. Stat (ISSN 2049-1573). 9(1) doi: 10.1002/sta4.285. 2020.
. Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling. The Journal of the Royal Statistical Society, Series C (Applied Statistics) (ISSN 0035-9254). 69(2) doi: 10.1111/rssc.12397. 2020.
. Detecting money laundering transactions with machine learning. Journal of Money Laundering Control (ISSN 1368-5201). 23(1) pp 173-186. doi: 10.1108/JMLC-07-2019-0055. 2020. Fulltekst
. Pairwise local Fisher and naive Bayes: Improving two standard discriminants. Journal of Econometrics (ISSN 0304-4076). 216(1) pp 284-304. doi: 10.1016/j.jeconom.2020.01.019. 2020. Arkiv
2018
. What price semiparametric Cox regression? Lifetime Data Analysis (ISSN 1380-7870). 25(3) pp 406-438. doi: 10.1007/s10985-018-9450-7. 2018. Fulltekst Arkiv
2017
. Parametric or nonparametric: The FIC approach. Statistica sinica (ISSN 1017-0405). 27(3) pp 951-981. doi: 10.5705/ss.202015.0364. 2017.
2016
. Bayesian AVO inversion to rock properties using a local neighborhood in a spatial prior model. The Leading Edge (ISSN 1070-485X). 35(5) pp 431-436. doi: 10.1190/tle35050431.1. 2016.
. A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties. Geophysics (ISSN 0016-8033). 81(3) pp R75-R87. doi: 10.1190/geo2015-0314.1. 2016.
2015
. A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties. Geophysics (ISSN 0016-8033). 81(3) pp R75-R87. doi: 10.1190/GEO2015-0314.1. 2015.
Academic chapter/article/Conference paper
2020
. Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees. In: Lecture Notes in Computer Science (LNCS). (ISBN 978-3-030-58805-2). pp 117-137. doi: 10.1007/978-3-030-57321-8_7. 2020. Arkiv
2015
. Parametric or nonparametric: the FIC approach for stationary time series. In: Proceedings of the 60th World Statistics Congress of the International Statistical Institute, ISI2015. (ISBN 978-90-73592-35-3). pp 4827-4832. 2015. Fulltekst
Academic lecture
2022
. Prediction Explanation with Shapley values. Explainable AI Seminars @ Imperial; Online, 2/3/2022.
2021
. Efficient Shapley value explanations through feature groups. The 28th Nordic Conference in Mathematical Statistics; Tromsø, Norway/ Online, 6/21/2021 - 6/24/2021.
. Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. 30th International Joint Conference on Artificial Intelligence; Online/Montreal, 8/19/2021 - 8/26/2021.
. Efficient and simple prediction explanations with groupShapley: A practical perspective. Italian Workshop on Explainable Artificial Intelligence 2021; Milano, Italy/ Online, 12/1/2021 - 12/3/2021.
2020
. How to open the black box – individual prediction explanation. Statistics seminar series at Department of Mathematical Sciences, NTNU; Trondheim, 3/9/2020.
2019
. Opening the black box -- individual prediction explanation. Big Insight Day 2020; Oslo, 11/14/2019.
. How to open the black box -- Individual prediction explanation. Det 20. norske statistikermøtet; Stavanger, 6/18/2019 - 6/20/2019.
2018
. Parametric or nonparametric, that’s the question. The FocuStat conference 2018, 5/22/2018 - 5/25/2018.
2017
. Estimating the seal pup abundance in the Greenland Sea with Bayesian hierarchical modeling. Det 17. norske statistikermøtet; Fredrikstad, 6/12/2017 - 6/15/2017.
. A focused model selection criterion for selecting among parametric and nonparametric models. Building Bridges at Bislett, 5/22/2017 - 5/24/2017.
. Bayesian modelling of cluster point process models. Spatial Statistics 2017; Lancaster, 7/4/2017 - 7/7/2017.
2016
. Estimating seal pup abundance with LGCP. Autumn Meeting on Latent Gaussian Models, 10/10/2016 - 10/11/2016.
. FIC with a nonparametric candidate – a new strategy for FIC construction. FICology; Oslo, 5/9/2016 - 5/11/2016.
2015
. An Approximate Bayesian Inversion Framework based on Local-gaussian Likelihoods. Petroleum Geostatistics; Biarritz, 9/7/2015 - 9/11/2015.
. An approximate Bayesian geophysical inversion framework based on local-Gaussian likelihoods. Oslo Graduate School in Biostatistics Workshop; Klækken, Hønefoss, 5/29/2015 - 5/30/2015.
. Parametric of Nonparametric: The FIC Approach for Time Series. 18th Norwegian Statistical Meeting; Solstrand, Bergen, 6/16/2015 - 6/18/2015. Omtale
2014
. Parametric or Nonparametric: The Focused Information Criterion Approach. 2014 Joint Statistical Meetings; Boston, 8/2/2014 - 8/7/2014.
2013
. Parametric or Nonparametric: The Focused Information Criterion Approach (+ Approximate Bayesian Inference). SFI-lunsj; Norsk Regnesentral, Oslo, 10/20/2013.
. Parametric or Nonparametric: The FIC Approach. 29-th European Meeting of Statisticians; Budapest, 7/20/2013 - 7/25/2103 1:00:00 AM.
. Parametric or Nonparametric: The FIC Approach. 17th Norwegian Statistical meeting; Halden, 6/11/2013 - 6/13/2013.
Abstract
2015
. An Approximate Bayesian Inversion Framework based on Local-Gaussian Likelihoods. EarthDoc. doi: 10.3997/2214-4609.201413634. 2015. Omtale
Lecture
2019
. Mindre rutinearbeid med maskinlæring -- Automatisk deteksjon av hvitvasking. Make Data Smart Again; Oslo, 5/9/2019.
2018
. XGBoost - efficient tree boosting. Big Insight lunch seminar, 1/31/2018.
. Detecting money laundering transactions – two stories. DNB Data Summit 2018; Oslo, 11/30/2018.
2016
. New focused approaches to topics within model selection and approximate Bayesian inversion. Disputas, 4/1/2016.
. Empirical likelihood. Trial lecture, PhD, 4/1/2016.
2013
. Approximate Bayesian Inference for Geophysical Inverse Problems. Oslo Graduate School in Biostatistics Workshop; Hønefoss, 5/24/2013 - 5/25/2013.
Poster
2021
. Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. The 30th International Joint Conference on Artificial Intelligence (IJCAI-21); Montreal (virtuelt), 8/19/2021 - 8/26/2021.
. groupSHAP: Efficient Shapley value explanation through feature groups. Geilo Winter School; Online, 1/25/2021 - 3/29/2021.
Doctoral dissertation
2016
. New focused approaches to topics within model selection and approximate Bayesian inversion. Faculty of Mathematics and Natural Sciences, University of Oslo. pp 185. 2016.
Masters thesis
2012
. Focused information criteria for selecting among parametric and nonparametric models.. 2012. Omtale
Report
2021
. groupShapley: Efficient prediction explanation with Shapley values for feature groups. Norsk Regnesentral. NR-notat SAMBA/20/21. 2021.
. Whitepaper on Exabel’s Factor Model. EXABEL. pp 7. 2021.
. White paper on performance evaluation of volatility estimation methods for Exabel. EXABEL. pp 12. 2021.
2019
. Shapley explanations using conditional inference trees. Norsk Regnesentral. NR-notat SAMBA/18/19. pp 33. 2019.
2018
. Detecting money laundering transactions -- which transactions should we learn from? Norsk Regnesentral. NR-notat SAMBA/06/18. pp 22. 2018.
. Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling. Norsk Regnesentral. NR-notat SAMBA/04/18. pp 25. 2018. Fulltekst
2017
. Statistical modeling of repertoire overlap in entire sampling spaces. Norsk Regnesentral. NR-notat SAMBA/13/2017. pp 15. 2017. Fulltekst
. Maskinlæring for vurdering av forsikringsrisiko. Norsk Regnesentral. NR-notat SAMBA/14/2017. pp 81. 2017.
2011
. Hypotesetesting av strekklengde for Seigmenn. Norsk Regnesentral. NR-notat SAMBA/25/11. pp 28. 2011.
. Vindprognoser og strømpriser. Norsk Regnesentral. NR-notat SAMBA/26/11. pp 21. 2011.
Popular scientific article
2011
Programme participation
2015
. Hvis man sjekker en billion kvinner, vil man finne fem slike par. 2015. Aftenposten TV [TV] 10/22/2015. Fulltekst
Website (informational material)
2015
. To liv: kvinnene i Lillestrøm som ble født på samme dag og døde på samme dag (FocuStat Blog Post). 2015. Fulltekst
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