22 citations · 32 across the 13 of their papers we have counts for
5 papers · 1 filter
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum +1
Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoretical…
MCCE: Monte Carlo sampling of realistic counterfactual explanations
Annabelle Redelmeier, Martin Jullum, Kjersti Aas +1
We introduce MCCE: Monte Carlo sampling of valid and realistic Counterfactual Explanations for tabular data, a novel counterfactual explanation method that generates on-manifold, a…
Discriminative Multimodal Learning via Conditional Priors in Generative Models
Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas +1
Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data. These two learning mechanisms can,…
groupShapley: Efficient prediction explanation with Shapley values for feature groups
Martin Jullum, Annabelle Redelmeier, Kjersti Aas
Shapley values has established itself as one of the most appropriate and theoretically sound frameworks for explaining predictions from complex machine learning models. The popular…
Explaining predictive models using Shapley values and non-parametric vine copulas
Kjersti Aas, Thomas Nagler, Martin Jullum +1
The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. If the features in reality are de…