73 citations · 108 across the 13 of their papers we have counts for
13 papers
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…
SAILS: Surrogate-based Analysis of Interactions via Local Effect Smooths
Timo Heiß, Julia Herbinger, Bernd Bischl +1
Feature interactions drive much of the predictive power of machine learning models, yet existing explanation methods only detect and quantify interactions without revealing their f…
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…
GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanations
Julia Herbinger, Gabriel Laberge, Maximilian Muschalik +3
Feature-based explanation methods aim to quantify how features influence the model's behavior, either locally or globally, but different methods often disagree, producing conflicti…
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier +2
Feature-based explanations, using perturbations or gradients, are a prevalent tool to understand decisions of black box machine learning models. Yet, differences between these meth…
Effector: A Python package for regional explanations
Vasilis Gkolemis, Christos Diou, Dimitris Kyriakopoulos +10
Effector is a Python package for interpreting machine learning (ML) models that are trained on tabular data through global and regional feature effects. Global effects, like Partia…
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration
Julian Rodemann, Federico Croppi, Philipp Arens +7
Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often consider…
Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation
Julia Herbinger, Susanne Dandl, Fiona K. Ewald +2
Surrogate models play a crucial role in retrospectively interpreting complex and powerful black box machine learning models via model distillation. This paper focuses on using mode…
Decomposing Global Feature Effects Based on Feature Interactions
Julia Herbinger, Marvin N. Wright, Thomas Nagler +2
Global feature effect methods, such as partial dependence plots, provide an intelligible visualization of the expected marginal feature effect. However, such global feature effect…
REPID: Regional Effect Plots with implicit Interaction Detection
Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio
Machine learning models can automatically learn complex relationships, such as non-linear and interaction effects. Interpretable machine learning methods such as partial dependence…
Explaining Hyperparameter Optimization via Partial Dependence Plots
Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio +2
Automated hyperparameter optimization (HPO) can support practitioners to obtain peak performance in machine learning models. However, there is often a lack of valuable insights int…
Grouped Feature Importance and Combined Features Effect Plot
Quay Au, Julia Herbinger, Clemens Stachl +2
Interpretable machine learning has become a very active area of research due to the rising popularity of machine learning algorithms and their inherently challenging interpretabili…
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Christoph Molnar, Gunnar König, Julia Herbinger +6
An increasing number of model-agnostic interpretation techniques for machine learning (ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) and S…