Kjersti Aas

According to our database, Kjersti Aas authored at least 24 papers between 1992 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Discriminative multimodal learning via conditional priors in generative models.

Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen

Neural Networks, January, 2024

2023

A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them.

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

CoRR, 2023

2022

Generating customer's credit behavior with deep generative models.

Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen

Knowl. Based Syst., 2022

Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features.

Lars H. B. Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas

J. Mach. Learn. Res., 2022

2021

Learning latent representations of bank customers with the Variational Autoencoder.

Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen

Expert Syst. Appl., 2021

MCCE: Monte Carlo sampling of realistic counterfactual explanations.

Annabelle Redelmeier, Martin Jullum, Kjersti Aas, Anders Løland

CoRR, 2021

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

Martin Jullum, Annabelle Redelmeier, Kjersti Aas

CoRR, 2021

Explaining predictive models using Shapley values and non-parametric vine copulas.

Kjersti Aas, Thomas Nagler, Martin Jullum, Anders Løland

CoRR, 2021

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

Kjersti Aas, Martin Jullum, Anders Løland

Artif. Intell., 2021

2020

Deep generative models for reject inference in credit scoring.

Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen

Knowl. Based Syst., 2020

Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees.

Annabelle Redelmeier, Martin Jullum, Kjersti Aas

Proceedings of the Machine Learning and Knowledge Extraction, 2020

2019

The evolution of a mobile payment solution network.

Kjersti Aas, Hanne Rognebakke

Netw. Sci., 2019

2018

Predicting mortgage default using convolutional neural networks.

Håvard Kvamme, Nikolai Sellereite, Kjersti Aas, Steffen Sjursen

Expert Syst. Appl., 2018

Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder.

Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen

CoRR, 2018

2016

Structure learning in Bayesian Networks using regular vines.

Ingrid Hobæk Haff, Kjersti Aas, Arnoldo Frigessi, Virginia Lacal

Comput. Stat. Data Anal., 2016

2010

On the simplified pair-copula construction - Simply useful or too simplistic?

Ingrid Hobæk Haff, Kjersti Aas, Arnoldo Frigessi

J. Multivar. Anal., 2010

1999

Applications of hidden Markov chains in image analysis.

Kjersti Aas, Line Eikvil, Ragnar Bang Huseby

Pattern Recognit., 1999

1997

Decoding bar codes from human-readable characters.

Kjersti Aas, Line Eikvil

Pattern Recognit. Lett., 1997

1996

Text page recognition using Grey-level features and hidden Markov models.

Kjersti Aas, Line Eikvil

Pattern Recognit., 1996

Automatic can separation.

Kjersti Aas, Line Eikvil, Otto Milvang

Proceedings of the 13th International Conference on Pattern Recognition, 1996

1995

Tools for interactive map conversion and vectorization.

Line Eikvil, Kjersti Aas, Hans Koren

Proceedings of the Third International Conference on Document Analysis and Recognition, 1995

Tools for Automatic Recognition of Character Strings in Maps.

Line Eikvil, Kjersti Aas, Marit Holden

Proceedings of the Computer Analysis of Images and Patterns, 6th International Conference, 1995

Text Recogniton from Grey Level Images Using Hidden Markovc Models.

Kjersti Aas, Line Eikvil, Tove Andersen

Proceedings of the Computer Analysis of Images and Patterns, 6th International Conference, 1995

1992

Combining range and intensity data with a hidden Markov model.

Ragnar Bang Huseby, Gutorm Thomas Hogasen, Geir Storvik, Kjersti Aas

Proceedings of the 11th IAPR International Conference on Pattern Recognition, 1992