22 citations · 32 across the 13 of their papers we have counts for
15 papers
How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional features
Mark A. van de Wiel, Jeroen Goedhart, Martin Jullum +1
In clinical prediction settings the importance of a high-dimensional feature like genomics is often assessed by evaluating the change in predictive performance when adding it to a…
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum +1
Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learnin…
Multimodal Generative Models for Bankruptcy Prediction Using Textual Data
Rogelio A. Mancisidor, Kjersti Aas
Textual data from financial filings, e.g., the Management's Discussion & Analysis (MDA) section in Form 10-K, has been used to improve the prediction accuracy of bankruptcy models.…
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…
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…