• 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 ...
    • Discriminative multimodal learning via conditional priors in generative models 

      Andrade Mancisidor, Rogelio; Kampffmeyer, Michael Christian; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2023)
      Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data, which depict an object from different viewpoints. These two learning mechanisms ...
    • 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 ...
    • 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 ...