Blar i NR vitenarkiv på forfatter "Aas, Kjersti"
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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) -
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 (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 ...