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Martin Jullum
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2020 – today
- 2024
- [j5]Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas:
A comparative study of methods for estimating model-agnostic Shapley value explanations. Data Min. Knowl. Discov. 38(4): 1782-1829 (2024) - [j4]Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland:
MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data. Data Min. Knowl. Discov. 38(4): 1830-1861 (2024) - 2023
- [c3]Martin Jullum, Jacob Sjødin, Robindra Prabhu, Anders Løland:
eXplego: An interactive Tool that Helps you Select Appropriate XAI-methods for your Explainability Needs. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2023: 146-151 - [i9]Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas:
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them. CoRR abs/2305.09536 (2023) - [i8]Fredrik Johannessen, Martin Jullum:
Finding Money Launderers Using Heterogeneous Graph Neural Networks. CoRR abs/2307.13499 (2023) - 2022
- [j3]Lars H. B. Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas:
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features. J. Mach. Learn. Res. 23: 213:1-213:51 (2022) - 2021
- [j2]Kjersti Aas, Martin Jullum, Anders Løland
:
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. Artif. Intell. 298: 103502 (2021) - [c2]Kary Främling
, Marcus Westberg
, Martin Jullum
, Manik Madhikermi
, Avleen Malhi
:
Comparison of Contextual Importance and Utility with LIME and Shapley Values. EXTRAAMAS@AAMAS 2021: 39-54 - [i7]Kjersti Aas, Thomas Nagler, Martin Jullum, Anders Løland:
Explaining predictive models using Shapley values and non-parametric vine copulas. CoRR abs/2102.06416 (2021) - [i6]Dag Tjøstheim, Martin Jullum, Anders Løland:
Statistical embedding: Beyond principal components. CoRR abs/2106.01858 (2021) - [i5]Martin Jullum, Annabelle Redelmeier, Kjersti Aas:
groupShapley: Efficient prediction explanation with Shapley values for feature groups. CoRR abs/2106.12228 (2021) - [i4]Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland:
MCCE: Monte Carlo sampling of realistic counterfactual explanations. CoRR abs/2111.09790 (2021) - [i3]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. CoRR abs/2111.13507 (2021) - 2020
- [c1]Annabelle Redelmeier, Martin Jullum, Kjersti Aas:
Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees. CD-MAKE 2020: 117-137 - [i2]Annabelle Redelmeier, Martin Jullum, Kjersti Aas:
Explaining predictive models with mixed features using Shapley values and conditional inference trees. CoRR abs/2007.01027 (2020)
2010 – 2019
- 2019
- [j1]Nikolai Sellereite
, Martin Jullum
:
shapr: An R-package for explaining machine learning models with dependence-aware Shapley values. J. Open Source Softw. 5(46): 2027 (2019) - [i1]Kjersti Aas, Martin Jullum, Anders Løland:
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. CoRR abs/1903.10464 (2019)
Coauthor Index
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