30 citations · 45 across the 4 of their papers we have counts for
4 papers · 1 filter
Do not explain without context: addressing the blind spot of model explanations
Katarzyna Woźnica, Katarzyna Pękala, Hubert Baniecki +3
The increasing number of regulations and expectations of predictive machine learning models, such as so called right to explanation, has led to a large number of methods promising…
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…
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets
Wojciech Kretowicz, Przemysław Biecek
Finding optimal hyperparameters for the machine learning algorithm can often significantly improve its performance. But how to choose them in a time-efficient way? In this paper we…