167 citations · 557 across the 21 of their papers we have counts for
9 papers · 1 filter
The Metagame of Interpretability and Meta-Attributions
Hubert Baniecki, Przemyslaw Biecek, Fabian Fumagalli
How can an arbitrary attribution method be generalized from first principles to capture interactions? We answer this with the metagame, a conceptual framework for quantifying secon…
shapiq: Shapley Interactions for Machine Learning
Maximilian Muschalik, Hubert Baniecki, Fabian Fumagalli +3
Originally rooted in game theory, the Shapley Value (SV) has recently become an important tool in machine learning research. Perhaps most notably, it is used for feature attributio…
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…
survex: an R package for explaining machine learning survival models
Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein +3
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread a…
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki +1
Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too…
Fooling Partial Dependence via Data Poisoning
Hubert Baniecki, Wojciech Kretowicz, Przemyslaw Biecek
Many methods have been developed to understand complex predictive models and high expectations are placed on post-hoc model explainability. It turns out that such explanations are…
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek +2
The increasing amount of available data, computing power, and the constant pursuit for higher performance results in the growing complexity of predictive models. Their black-box na…
The Grammar of Interactive Explanatory Model Analysis
Hubert Baniecki, Dariusz Parzych, Przemyslaw Biecek
The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? I…