38 citations · 51 across the 15 of their papers we have counts for
7 papers · 1 filter
Efficient and Accurate Explanation Estimation with Distribution Compression
Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl +1
We discover a theoretical connection between explanation estimation and distribution compression that significantly improves the approximation of feature attributions, importance,…
On the Robustness of Global Feature Effect Explanations
Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl +1
We study the robustness of global post-hoc explanations for predictive models trained on tabular data. Effects of predictor features in black-box supervised learning are an essenti…
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger +7
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to un…
mlr3summary: Concise and interpretable summaries for machine learning models
Susanne Dandl, Marc Becker, Bernd Bischl +2
This work introduces a novel R package for concise, informative summaries of machine learning models. We take inspiration from the summary function for (generalized) linear models…
A Guide to Feature Importance Methods for Scientific Inference
Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright +3
While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding…
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…