publications

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

  1. survex: an R package for explaining machine learning survival models
    M. Spytek, M. Krzyzinski, S. H. Langbein, H. Baniecki, M. N. Wright, P. Biecek
    arXiv:2308.16113v1
  2. Explaining and visualizing black-box models through counterfactual paths
    B. Pfeifer, M. Krzyzinski, H. Baniecki, A. Saranti, A. Holzinger, P. Biecek
    arXiv:2307.07764v3
  3. IJCAI workshop
    Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey
    H. Baniecki, P. Biecek
    arXiv:2306.06123v1, IJCAI 2023 Workshop on XAI
  4. Performance is not enough: the story told by a Rashomon quartet
    P. Biecek, H. Baniecki, M. Krzyzinski, D. Cook
    arXiv:2302.13356v3

preprints

refereed

  1. Towards Evaluating Explanations of Vision Transformers for Medical Imaging
    P. Komorowski, H. Baniecki, P. Biecek
    CVPR 2023 Workshops
  2. 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
  3. Kno. Sys.
    SurvSHAP(t): Time-dependent explanations of machine learning survival models
    M. Krzyzinski, M. Spytek, H. Baniecki, P. Biecek
    Knowledge-Based Systems, 2023
  4. The grammar of interactive explanatory model analysis
    H. Baniecki, D. Parzych, P. Biecek
    Data Mining and Knowledge Discovery, 2023
    Interactive analysis of a model improves the performance of human decision making.
  5. Multi-omics disease module detection with an explainable Greedy Decision Forest
    B. Pfeifer, H. Baniecki, A. Saranti, P. Biecek, A. Holzinger
    Scientific Reports, 2022
  6. 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.
  7. Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)
    H. Baniecki, P. Biecek
    AAAI 2022
  8. 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
    For this work, I received the 2022 John M. Chambers Statistical Software Award.
  9. Responsible Prediction Making of COVID-19 Mortality (Student Abstract)
    H. Baniecki, P. Biecek
    AAAI 2021
  10. modelStudio: Interactive studio with explanations for ML predictive models
    H. Baniecki, P. Biecek
    Journal of Open Source Software, 2019