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ORCIDHubert Baniecki
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- affiliation: University of Warsaw, Poland
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2020 – today
- 2025
[j7]Hubert Baniecki
, Bartlomiej Sobieski, Patryk Szatkowski, Przemyslaw Bombinski, Przemyslaw Biecek:
Interpretable machine learning for time-to-event prediction in medicine and healthcare. Artif. Intell. Medicine 159: 103026 (2025)- 2024
- [j6]Hubert Baniecki, Dariusz Parzych, Przemyslaw Biecek:
The grammar of interactive explanatory model analysis. Data Min. Knowl. Discov. 38(5): 2596-2632 (2024) - [j5]Hubert Baniecki, Przemyslaw Biecek:
Adversarial attacks and defenses in explainable artificial intelligence: A survey. Inf. Fusion 107: 102303 (2024) - [c7]Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski, Jakub Szulc, Michal Janik, Hubert Baniecki, Przemyslaw Biecek:
Red-Teaming Segment Anything Model. CVPR Workshops 2024: 2947-2956 - [c6]Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
On the Robustness of Global Feature Effect Explanations. ECML/PKDD (2) 2024: 125-142 - [i19]Vladimir Zaigrajew, Hubert Baniecki, Lukasz Tulczyjew, Agata M. Wijata, Jakub Nalepa, Nicolas Longépé, Przemyslaw Biecek:
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI. CoRR abs/2403.08017 (2024) - [i18]Sophie Hanna Langbein, Mateusz Krzyzinski, Mikolaj Spytek, Hubert Baniecki, Przemyslaw Biecek, Marvin N. Wright:
Interpretable Machine Learning for Survival Analysis. CoRR abs/2403.10250 (2024) - [i17]Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski, Jakub Szulc, Michal Janik, Hubert Baniecki, Przemyslaw Biecek:
Red-Teaming Segment Anything Model. CoRR abs/2404.02067 (2024) - [i16]Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
On the Robustness of Global Feature Effect Explanations. CoRR abs/2406.09069 (2024) - [i15]Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
Efficient and Accurate Explanation Estimation with Distribution Compression. CoRR abs/2406.18334 (2024) - [i14]Maciej Chrabaszcz, Hubert Baniecki, Piotr Komorowski, Szymon Plotka, Przemyslaw Biecek:
Aggregated Attributions for Explanatory Analysis of 3D Segmentation Models. CoRR abs/2407.16653 (2024) - [i13]Maximilian Muschalik, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki, Barbara Hammer, Eyke Hüllermeier:
shapiq: Shapley Interactions for Machine Learning. CoRR abs/2410.01649 (2024) - 2023
- [j4]Mikolaj Spytek, Mateusz Krzyzinski, Sophie Hanna Langbein, Hubert Baniecki, Marvin N. Wright, Przemyslaw Biecek:
survex: an R package for explaining machine learning survival models. Bioinform. 39(12) (2023) - [j3]Mateusz Krzyzinski, Mikolaj Spytek, Hubert Baniecki, Przemyslaw Biecek:
SurvSHAP(t): Time-dependent explanations of machine learning survival models. Knowl. Based Syst. 262: 110234 (2023) - [c5]Hubert Baniecki, Bartlomiej Sobieski, Przemyslaw Bombinski, Patryk Szatkowski, Przemyslaw Biecek:
Hospital Length of Stay Prediction Based on Multi-modal Data Towards Trustworthy Human-AI Collaboration in Radiomics. AIME 2023: 65-74 - [c4]Piotr Komorowski, Hubert Baniecki, Przemyslaw Biecek:
Towards Evaluating Explanations of Vision Transformers for Medical Imaging. CVPR Workshops 2023: 3726-3732 - [i12]Przemyslaw Biecek, Hubert Baniecki, Mateusz Krzyzinski:
Performance is not enough: a story of the Rashomon's quartet. CoRR abs/2302.13356 (2023) - [i11]Hubert Baniecki, Bartlomiej Sobieski, Przemyslaw Bombinski, Patryk Szatkowski, Przemyslaw Biecek:
Hospital Length of Stay Prediction Based on Multi-modal Data towards Trustworthy Human-AI Collaboration in Radiomics. CoRR abs/2303.09817 (2023) - [i10]Piotr Komorowski, Hubert Baniecki, Przemyslaw Biecek:
Towards Evaluating Explanations of Vision Transformers for Medical Imaging. CoRR abs/2304.06133 (2023) - [i9]Hubert Baniecki, Przemyslaw Biecek:
Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey. CoRR abs/2306.06123 (2023) - [i8]Bastian Pfeifer, Mateusz Krzyzinski, Hubert Baniecki, Anna Saranti, Andreas Holzinger, Przemyslaw Biecek:
Explainable AI with counterfactual paths. CoRR abs/2307.07764 (2023) - [i7]Mikolaj Spytek, Mateusz Krzyzinski, Sophie Hanna Langbein, Hubert Baniecki, Marvin N. Wright, Przemyslaw Biecek:
survex: an R package for explaining machine learning survival models. CoRR abs/2308.16113 (2023) - [i6]Hubert Baniecki, Maciej Chrabaszcz, Andreas Holzinger, Bastian Pfeifer, Anna Saranti, Przemyslaw Biecek:
Be Careful When Evaluating Explanations Regarding Ground Truth. CoRR abs/2311.04813 (2023) - 2022
- [c3]Hubert Baniecki, Przemyslaw Biecek:
Manipulating SHAP via Adversarial Data Perturbations (Student Abstract). AAAI 2022: 12907-12908 - [c2]Hubert Baniecki, Wojciech Kretowicz, Przemyslaw Biecek:
Fooling Partial Dependence via Data Poisoning. ECML/PKDD (3) 2022: 121-136 - [i5]Mateusz Krzyzinski, Mikolaj Spytek, Hubert Baniecki, Przemyslaw Biecek:
SurvSHAP(t): Time-dependent explanations of machine learning survival models. CoRR abs/2208.11080 (2022) - 2021
- [j2]Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, Przemyslaw Biecek:
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python. J. Mach. Learn. Res. 22: 214:1-214:7 (2021) - [c1]Hubert Baniecki, Przemyslaw Biecek:
Responsible Prediction Making of COVID-19 Mortality (Student Abstract). AAAI 2021: 15755-15756 - [i4]Hubert Baniecki, Wojciech Kretowicz, Przemyslaw Biecek:
Fooling Partial Dependence via Data Poisoning. CoRR abs/2105.12837 (2021) - [i3]Katarzyna Woznica, Katarzyna Pekala, Hubert Baniecki, Wojciech Kretowicz, Elzbieta Sienkiewicz, Przemyslaw Biecek:
Do not explain without context: addressing the blind spot of model explanations. CoRR abs/2105.13787 (2021) - 2020
- [i2]Hubert Baniecki, Przemyslaw Biecek:
The Grammar of Interactive Explanatory Model Analysis. CoRR abs/2005.00497 (2020) - [i1]Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, Przemyslaw Biecek:
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python. CoRR abs/2012.14406 (2020)
2010 – 2019
- 2019
- [j1]Hubert Baniecki, Przemyslaw Biecek:
modelStudio: Interactive Studio with Explanations for ML Predictive Models. J. Open Source Softw. 4(43): 1798 (2019)
Coauthor Index
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