1 code implementation • 15 Mar 2024 • Sophie Hanna Langbein, Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki, Przemysław Biecek, Marvin N. Wright
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 become increasingly important over the last decade.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +4
1 code implementation • 30 Aug 2023 • Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein, Hubert Baniecki, Marvin N. Wright, Przemysław Biecek
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models.
1 code implementation • 22 Aug 2023 • Katarzyna Kobylińska, Mateusz Krzyziński, Rafał Machowicz, Mariusz Adamek, Przemysław Biecek
If differently behaving models are detected in the Rashomon set, their combined analysis leads to more trustworthy conclusions, which is of vital importance for high-stakes applications such as medical applications.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 25 Feb 2023 • Piotr Wilczyński, Artur Żółkowski, Mateusz Krzyziński, Emilia Wiśnios, Bartosz Pieliński, Stanisław Giziński, Julian Sienkiewicz, Przemysław Biecek
This paper introduces HADES, a novel tool for automatic comparative documents with similar structures.
no code implementations • 10 Nov 2022 • Artur Żółkowski, Mateusz Krzyziński, Piotr Wilczyński, Stanisław Giziński, Emilia Wiśnios, Bartosz Pieliński, Julian Sienkiewicz, Przemysław Biecek
The number of standardized policy documents regarding climate policy and their publication frequency is significantly increasing.
1 code implementation • 23 Aug 2022 • Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki, Przemysław Biecek
Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME.