167 citations · 1k across the 73 of their papers we have counts for
26 papers · 1 filter
Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning
Hubert Baniecki, Przemyslaw Biecek
A common belief is that intrinsically interpretable deep learning models ensure a correct, intuitive understanding of their behavior and offer greater robustness against accidental…
Global Counterfactual Directions
Bartlomiej Sobieski, Przemysław Biecek
Despite increasing progress in development of methods for generating visual counterfactual explanations, especially with the recent rise of Denoising Diffusion Probabilistic Models…
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
Weronika Hryniewska-Guzik, Bartosz Sawicki, Przemysław Biecek
This paper presents a comprehensive comparative analysis of explainable artificial intelligence (XAI) ensembling methods. Our research brings three significant contributions. First…
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data
Katarzyna Kobylińska, Mateusz Krzyziński, Rafał Machowicz +2
The machine learning modeling process conventionally culminates in selecting a single model that maximizes a selected performance metric. However, this approach leads to abandoning…
Glocal Explanations of Expected Goal Models in Soccer
Mustafa Cavus, Adrian Stando, Przemyslaw Biecek
The expected goal models have gained popularity, but their interpretability is often limited, especially when trained using black-box methods. Explainable artificial intelligence t…
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…