Combining range and intensity data with a hidden Markov model. - Ragnar Bang Huseby; Gutorm Thomas Hogasen; Geir Storvik; Kjersti Aas (ICPR (2), 1992)

Text Recogniton from Grey Level Images Using Hidden Markovc Models. - Kjersti Aas; Line Eikvil; Tove Andersen (CAIP, 1995)

Tools for Automatic Recognition of Character Strings in Maps. - Line Eikvil; Kjersti Aas; Marit Holden (CAIP, 1995)

Tools for interactive map conversion and vectorization. - Line Eikvil; Kjersti Aas; Hans Koren (ICDAR, 1995)

Automatic can separation. - Kjersti Aas; Line Eikvil; Otto Milvang (ICPR, 1996)

Text page recognition using Grey-level features and hidden Markov models. - Kjersti Aas; Line Eikvil (Pattern Recognit., 1996)

Decoding bar codes from human-readable characters. - Kjersti Aas; Line Eikvil (Pattern Recognit. Lett., 1997)

Applications of hidden Markov chains in image analysis. - Kjersti Aas; Line Eikvil; Ragnar Bang Huseby (Pattern Recognit., 1999)

On the simplified pair-copula construction - Simply useful or too simplistic? - Ingrid Hobæk Haff; Kjersti Aas; Arnoldo Frigessi (J. Multivar. Anal., 2010)

Structure learning in Bayesian Networks using regular vines. - Ingrid Hobæk Haff; Kjersti Aas; Arnoldo Frigessi; Virginia Lacal (Comput. Stat. Data Anal., 2016)

Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder. - Rogelio Andrade Mancisidor; Michael Kampffmeyer; Kjersti Aas; Robert Jenssen (CoRR, 2018)

Predicting mortgage default using convolutional neural networks. - Håvard Kvamme; Nikolai Sellereite; Kjersti Aas; Steffen Sjursen (Expert Syst. Appl., 2018)

The evolution of a mobile payment solution network. - Kjersti Aas; Hanne Rognebakke (Netw. Sci., 2019)

Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees. - Annabelle Redelmeier; Martin Jullum; Kjersti Aas (CD-MAKE, 2020)

Deep generative models for reject inference in credit scoring. - Rogelio Andrade Mancisidor; Michael Kampffmeyer; Kjersti Aas; Robert Jenssen (Knowl. Based Syst., 2020)

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. - Kjersti Aas; Martin Jullum; Anders Løland (Artif. Intell., 2021)

Explaining predictive models using Shapley values and non-parametric vine copulas. - Kjersti Aas; Thomas Nagler; Martin Jullum; Anders Løland (CoRR, 2021)

groupShapley: Efficient prediction explanation with Shapley values for feature groups. - Martin Jullum; Annabelle Redelmeier; Kjersti Aas (CoRR, 2021)

MCCE: Monte Carlo sampling of realistic counterfactual explanations. - Annabelle Redelmeier; Martin Jullum; Kjersti Aas; Anders Løland (CoRR, 2021)

Learning latent representations of bank customers with the Variational Autoencoder. - Rogelio Andrade Mancisidor; Michael Kampffmeyer; Kjersti Aas; Robert Jenssen (Expert Syst. Appl., 2021)

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)

Generating customer's credit behavior with deep generative models. - Rogelio Andrade Mancisidor; Michael Kampffmeyer; Kjersti Aas; Robert Jenssen (Knowl. Based Syst., 2022)

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)

Discriminative multimodal learning via conditional priors in generative models. - Rogelio Andrade Mancisidor; Michael Kampffmeyer; Kjersti Aas; Robert Jenssen (Neural Networks, 2024)

A comparative study of methods for estimating model-agnostic Shapley value explanations. - Lars Henry Berge Olsen; Ingrid Kristine Glad; Martin Jullum; Kjersti Aas (Data Min. Knowl. Discov., 2024)

MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data. - Annabelle Redelmeier; Martin Jullum; Kjersti Aas; Anders Løland (Data Min. Knowl. Discov., 2024)