Kjersti Aas

32 papers A 1B 1C 3Journal 26Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional features.
CoRR
Mark A. van de Wiel, Jeroen M. Goedhart, Martin Jullum, Kjersti Aas
2024 J jnl
A comparative study of methods for estimating model-agnostic Shapley value explanations.
Data Min. Knowl. Discov.
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas
2024 J jnl
Discriminative multimodal learning via conditional priors in generative models.
Neural Networks
Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen
2024 J jnl
MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data.
Data Min. Knowl. Discov.
Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland
2023 J jnl
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them.
CoRR
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas
2022 J jnl
Generating customer's credit behavior with deep generative models.
Knowl. Based Syst.
Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen
2022 J jnl
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features.
J. Mach. Learn. Res.
Lars H. B. Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas
2021 J jnl
Discriminative Multimodal Learning via Conditional Priors in Generative Models.
CoRR
Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen
2021 J jnl
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values.
Artif. Intell.
Kjersti Aas, Martin Jullum, Anders Løland
2021 J jnl
Explaining predictive models using Shapley values and non-parametric vine copulas.
CoRR
Kjersti Aas, Thomas Nagler, Martin Jullum, Anders Løland
2021 J jnl
Learning latent representations of bank customers with the Variational Autoencoder.
Expert Syst. Appl.
Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen
2021 J jnl
MCCE: Monte Carlo sampling of realistic counterfactual explanations.
CoRR
Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland
2021 J jnl
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features.
CoRR
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas
2021 J jnl
groupShapley: Efficient prediction explanation with Shapley values for feature groups.
CoRR
Martin Jullum, Annabelle Redelmeier, Kjersti Aas
2020 J jnl
Deep generative models for reject inference in credit scoring.
Knowl. Based Syst.
Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen
2020 C conf
Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees.
CD-MAKE
Annabelle Redelmeier, Martin Jullum, Kjersti Aas
2020 J jnl
Explaining predictive models with mixed features using Shapley values and conditional inference trees.
CoRR
Annabelle Redelmeier, Martin Jullum, Kjersti Aas
2019 J jnl
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values.
CoRR
Kjersti Aas, Martin Jullum, Anders Løland
2019 J jnl
Learning Latent Representations of Bank Customers With The Variational Autoencoder.
CoRR
Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen
2019 J jnl
The evolution of a mobile payment solution network.
Netw. Sci.
Kjersti Aas, Hanne Rognebakke
2018 J jnl
Predicting mortgage default using convolutional neural networks.
Expert Syst. Appl.
Håvard Kvamme, Nikolai Sellereite, Kjersti Aas, Steffen Sjursen
2018 J jnl
Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder.
CoRR
Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen
2016 J jnl
Structure learning in Bayesian Networks using regular vines.
Comput. Stat. Data Anal.
Ingrid Hobæk Haff, Kjersti Aas, Arnoldo Frigessi, Virginia Lacal
2010 J jnl
On the simplified pair-copula construction - Simply useful or too simplistic?
J. Multivar. Anal.
Ingrid Hobæk Haff, Kjersti Aas, Arnoldo Frigessi
1999 J jnl
Applications of hidden Markov chains in image analysis.
Pattern Recognit.
Kjersti Aas, Line Eikvil, Ragnar Bang Huseby
1997 J jnl
Decoding bar codes from human-readable characters.
Pattern Recognit. Lett.
Kjersti Aas, Line Eikvil
1996 B conf
Automatic can separation.
ICPR
Kjersti Aas, Line Eikvil, Otto Milvang
1996 J jnl
Text page recognition using Grey-level features and hidden Markov models.
Pattern Recognit.
Kjersti Aas, Line Eikvil
1995 C conf
Text Recogniton from Grey Level Images Using Hidden Markovc Models.
CAIP
Kjersti Aas, Line Eikvil, Tove Andersen
1995 C conf
Tools for Automatic Recognition of Character Strings in Maps.
CAIP
Line Eikvil, Kjersti Aas, Marit Holden
1995 A conf
Tools for interactive map conversion and vectorization.
ICDAR
Line Eikvil, Kjersti Aas, Hans Koren
1992 — conf
Combining range and intensity data with a hidden Markov model.
ICPR (2)
Ragnar Bang Huseby, Gutorm Thomas Hogasen, Geir Storvik, Kjersti Aas