2 citations · 2 across the 5 of their papers we have counts for
7 papers
Exabel's Factor Model
Øyvind Grotmol, Michael Scheuerer, Kjersti Aas +1
Factor models have become a common and valued tool for understanding the risks associated with an investing strategy. In this report we describe Exabel's factor model, we quantify…
Performance evaluation of volatility estimation methods for Exabel
Øyvind Grotmol, Martin Jullum, Kjersti Aas +1
Quantifying both historic and future volatility is key in portfolio risk management. This note presents and compares estimation strategies for volatility estimation in an estimatio…
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…
Explaining predictive models with mixed features using Shapley values and conditional inference trees
Annabelle Redelmeier, Martin Jullum, Kjersti Aas
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…
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Kjersti Aas, Martin Jullum, Anders Løland
Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from a complex machine learning model by…