Browsing Publikasjoner fra Cristin by Author "Aas, Kjersti"
Now showing items 1-10 of 10
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Deep generative models for reject inference in credit scoring
Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2020)Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of ... -
Efficient and simple prediction explanations with groupShapley: A practical perspective
Jullum, Martin; Redelmeier, Annabelle Alice; Aas, Kjersti (Peer reviewed; Journal article, 2021) -
The evolution of a mobile payment solution network
Aas, Kjersti; Rognebakke, Hanne Therese Wist (Journal article; Peer reviewed, 2019)Vipps is a peer-to-peer mobile payment solution launched by Norway’s largest financial services group DNB. The Vipps transaction data may be viewed as a graph with users corresponding to the nodes, and the financial ... -
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Aas, Kjersti; Jullum, Martin; Løland, Anders (Journal article; Peer reviewed, 2021)Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from such models by learning simple, interpretable explanations. Shapley value is ... -
Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees
Redelmeier, Annabelle Alice; Jullum, Martin; Aas, Kjersti (Chapter, 2002)It is becoming increasingly important to explain complex, black-box machine learning models. Although there is an expanding literature on this topic, Shapley values stand out as a sound method to explain predictions from ... -
Generating customer's credit behavior with deep generative models
Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2022)Innovation is considered essential for today's organizations to survive and thrive. Researchers have also stressed the importance of leadership as a driver of followers' innovative work behavior (FIB). Yet, despite a large ... -
groupShapley: Efficient prediction explanation with Shapley values for feature groups
Jullum, Martin; Redelmeier, Annabelle Alice; Aas, Kjersti (NR-notat;, Research report, 2021) -
groupShapley: Efficient prediction explanation with Shapley values for feature groups
Jullum, Martin; Redelmeier, Annabelle Alice; Aas, Kjersti (Journal article, 2021) -
Learning latent representations of bank customers with the Variational Autoencoder
Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2020)Learning data representations that reflect the customers’ creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In ... -
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features
Olsen, Lars Henry Berge; Glad, Ingrid Kristine; Jullum, Martin; Aas, Kjersti (Journal article; Peer reviewed, 2022)Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoretical properties and a sound mathematical ...