| dalex: Responsible machine learning with interactive explainability and fairness in Python H Baniecki, W Kretowicz, P Piatyszek, J Wisniewski, P Biecek Journal of Machine Learning Research 22 (214), 1–7, 2021 | 144 | 2021 |
| SurvSHAP(t): Time-dependent explanations of machine learning survival models M Krzyziński, M Spytek, H Baniecki, P Biecek Knowledge-Based Systems 262, 110234, 2023 | 88 | 2023 |
| Adversarial attacks and defenses in explainable artificial intelligence: A survey H Baniecki, P Biecek Information Fusion 107, 102303, 2024 | 80 | 2024 |
| The grammar of interactive explanatory model analysis H Baniecki, D Parzych, P Biecek Data Mining and Knowledge Discovery 38 (5), 2596–2632, 2023 | 49 | 2023 |
| Towards evaluating explanations of vision transformers for medical imaging P Komorowski, H Baniecki, P Biecek IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR …, 2023 | 33 | 2023 |
| Fooling partial dependence via data poisoning H Baniecki, W Kretowicz, P Biecek European Conference on Machine Learning (ECML PKDD), 121–136, 2022 | 28 | 2022 |
| Multi-omics disease module detection with an explainable greedy decision forest B Pfeifer, H Baniecki, A Saranti, P Biecek, A Holzinger Scientific Reports 12 (1), 1–15, 2022 | 25 | 2022 |
| modelStudio: Interactive studio with explanations for ML predictive models H Baniecki, P Biecek Journal of Open Source Software 4 (43), 1798, 2020 | 25 | 2020 |
| survex: An R package for explaining machine learning survival models M Spytek, M Krzyziński, SH Langbein, H Baniecki, MN Wright, P Biecek Bioinformatics 39 (12), btad723, 2023 | 21* | 2023 |
| ingredients: Effects and importances of model ingredients P Biecek, H Baniecki R package version 2.3, 2020 | 17* | 2020 |
| Manipulating SHAP via adversarial data perturbations (student abstract) H Baniecki, P Biecek AAAI Conference on Artificial Intelligence 36 (11), 12907–12908, 2022 | 10 | 2022 |
| Interpretable machine learning for time-to-event prediction in medicine and healthcare H Baniecki, B Sobieski, P Szatkowski, P Bombinski, P Biecek Artificial Intelligence in Medicine 159, 103026, 2025 | 8* | 2025 |
| shapiq: Shapley interactions for machine learning M Muschalik, H Baniecki, F Fumagalli, P Kolpaczki, B Hammer, ... Advances in Neural Information Processing Systems (NeurIPS) 37, 130324–130357, 2024 | 8 | 2024 |
| Performance is not enough: The story told by a Rashomon quartet P Biecek, H Baniecki, M Krzyzinski, D Cook Journal of Computational and Graphical Statistics 33 (3), 1118–1121, 2024 | 6* | 2024 |
| Interpretable machine learning for survival analysis SH Langbein, M Krzyziński, M Spytek, H Baniecki, P Biecek, MN Wright arXiv preprint arXiv:2403.10250, 2024 | 5 | 2024 |
| Efficient and accurate explanation estimation with distribution compression H Baniecki, G Casalicchio, B Bischl, P Biecek International Conference on Learning Representations (ICLR), Spotlight, 2025 | 2 | 2025 |
| Responsible prediction making of COVID-19 mortality (student abstract) H Baniecki, P Biecek AAAI Conference on Artificial Intelligence 35 (18), 15755–15756, 2021 | 2 | 2021 |
| On the robustness of global feature effect explanations H Baniecki, G Casalicchio, B Bischl, P Biecek European Conference on Machine Learning (ECML PKDD), 125–142, 2024 | 1 | 2024 |
| Red-teaming segment anything model K Jankowski, B Sobieski, M Kwiatkowski, J Szulc, M Janik, H Baniecki, ... IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR …, 2024 | 1 | 2024 |
| Red teaming models for hyperspectral image analysis using explainable AI V Zaigrajew, H Baniecki, L Tulczyjew, AM Wijata, J Nalepa, N Longépé, ... ICLR Workshops, 2024 | 1 | 2024 |
Przemyslaw BiecekWarsaw University of TechnologyVerified email at mimuw.edu.pl