find me @hbaniecki
My research interests include explainable machine learning, responsibility, evaluation and adversarial attacks.
Developing and maintaining open-source Python & R packages used for explainability of predictive models.
|2022-Jun new paper||
Fooling Partial Dependence via Data PoisoningAccepted for publication at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD’22).
Admissioned to PhD in Computer Science at the University of Warsaw and got funded by the project
|2022-Mar||Received the Minister’s (of Education and Science, Poland) scholarship for students for significant scientific achievements.|
|2021-Oct new paper||
Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)Accepted for publication at the AAAI Conference on Artificial Intelligence (AAAI’22).
- ECML PKDD
BScFooling Partial Dependence via Data PoisoningIn: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2022
Note: This paper is the core of our BSc thesis defended at Warsaw University of Technology.
SAManipulating SHAP via Adversarial Data Perturbations (Student Abstract)In: AAAI Conference on Artificial Intelligence (AAAI), 2022
MLOSSdalex: Responsible Machine Learning with Interactive Explainability and Fairness in PythonJournal of Machine Learning Research, 2021