54 citations · 61 across the 7 of their papers we have counts for
6 papers · 1 filter
A Hilbert-Valued Functional Decomposition Framework for Explaining Time-Dependent Outputs
Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright +1
Feature-based explanations quantify features' influence on model predictions, but are primarily designed for scalar outputs. In many applications, however, outputs are functional o…
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Sophie Hanna Langbein, Hubert Baniecki, Fabian Fumagalli +3
Hazard and survival functions are natural, interpretable targets in time-to-event prediction, but their inherent non-additivity fundamentally limits standard additive explanation m…
Machine Learning in Epidemiology
Marvin N. Wright, Lukas Burk, Pegah Golchian +3
In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools…
Reduction Techniques for Survival Analysis
Johannes Piller, Léa Orsini, Simon Wiegrebe +6
In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that "reduce" a survival task to a more common regression or classifica…
Gradient-based Explanations for Deep Learning Survival Models
Sophie Hanna Langbein, Niklas Koenen, Marvin N. Wright
Deep learning survival models often outperform classical methods in time-to-event predictions, particularly in personalized medicine, but their "black box" nature hinders broader a…
Interpretable Machine Learning for Survival Analysis
Sophie Hanna Langbein, Mateusz Krzyziński, Mikołaj Spytek +3
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has becom…