preprints in review, journal articles, and papers published in conference proceedings

  1. Efficient and accurate explanation estimation with distribution compression
    H. Baniecki, G. Casalicchio, B. Bischl, P. Biecek
    ICML 2024 Workshops
  2. Interpretable machine learning for survival analysis
    S. H. Langbein, M. Krzyzinski, M. Spytek, H. Baniecki, P. Biecek, M. N. Wright
  3. Red teaming models for hyperspectral image analysis using explainable AI
    V. Zaigrajew, H. Baniecki, L. Tulczyjew, A. M. Wijata, J. Nalepa, N. Longepe, P. Biecek
    ICLR 2024 Workshops

preprints / in review / non-archival

accepted / in press / published

  1. On the robustness of global feature effect explanations
    H. Baniecki, G. Casalicchio, B. Bischl, P. Biecek
    ECML PKDD 2024
    Theoretical bounds for the robustness of feature effects to data and model perturbations.
  2. 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, 2024
  3. Red-teaming segment anything model
    K. Jankowski=, B. Sobieski=, M. Kwiatkowski, J. Szulc, M. Janik, H. Baniecki, P. Biecek
    CVPR 2024 Workshops
  4. Adversarial attacks and defenses in explainable artificial intelligence: A survey
    H. Baniecki, P. Biecek
    Information Fusion, 2024
    Review of adversarial attacks on explanations and how to defend them.
  5. survex: an R package for explaining machine learning survival models
    M. Spytek, M. Krzyzinski, S. H. Langbein, H. Baniecki, M. N. Wright, P. Biecek
    Bioinformatics, 2023
  6. Towards evaluating explanations of vision transformers for medical imaging
    P. Komorowski, H. Baniecki, P. Biecek
    CVPR 2023 Workshops
  7. Hospital length of stay prediction based on multi-modal data towards trustworthy human-AI collaboration in radiomics
    H. Baniecki, B. Sobieski, P. Bombinski, P. Szatkowski, P. Biecek
    AIME 2023
  8. SurvSHAP(t): Time-dependent explanations of machine learning survival models
    M. Krzyzinski, M. Spytek, H. Baniecki, P. Biecek
    Knowledge-Based Systems, 2023
  9. The grammar of interactive explanatory model analysis
    H. Baniecki, D. Parzych, P. Biecek
    Data Mining and Knowledge Discovery, 2023
    Interactive explanation of a model improves the performance of human decision making.
  10. Multi-omics disease module detection with an explainable greedy decision forest
    B. Pfeifer, H. Baniecki, A. Saranti, P. Biecek, A. Holzinger
    Scientific Reports, 2022
  11. Fooling partial dependence via data poisoning
    H. Baniecki, W. Kretowicz, P. Biecek
    ECML PKDD 2022
    Feature effect explanations can be manipulated in an adversarial manner.
  12. Manipulating SHAP via adversarial data perturbations (student abstract)
    H. Baniecki, P. Biecek
    AAAI 2022
  13. 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, 2021
    2022 John M. Chambers Statistical Software Award
  14. Responsible prediction making of COVID-19 mortality (student abstract)
    H. Baniecki, P. Biecek
    AAAI 2021
  15. modelStudio: Interactive studio with explanations for ML predictive models
    H. Baniecki, P. Biecek
    Journal of Open Source Software, 2019