Hubert Baniecki

Orcid: 0000-0001-6661-5364

According to our database1, Hubert Baniecki authored at least 17 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
Interpretable Machine Learning for Survival Analysis.
CoRR, 2024

Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI.
CoRR, 2024

2023
survex: an R package for explaining machine learning survival models.
Bioinform., December, 2023

SurvSHAP(t): Time-dependent explanations of machine learning survival models.
Knowl. Based Syst., February, 2023

Be Careful When Evaluating Explanations Regarding Ground Truth.
CoRR, 2023

Explainable AI with counterfactual paths.
CoRR, 2023

Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey.
CoRR, 2023

Performance is not enough: a story of the Rashomon's quartet.
CoRR, 2023

Towards Evaluating Explanations of Vision Transformers for Medical Imaging.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Hospital Length of Stay Prediction Based on Multi-modal Data Towards Trustworthy Human-AI Collaboration in Radiomics.
Proceedings of the Artificial Intelligence in Medicine, 2023

2022
Fooling Partial Dependence via Data Poisoning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Manipulating SHAP via Adversarial Data Perturbations (Student Abstract).
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python.
J. Mach. Learn. Res., 2021

Do not explain without context: addressing the blind spot of model explanations.
CoRR, 2021

Responsible Prediction Making of COVID-19 Mortality (Student Abstract).
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
The Grammar of Interactive Explanatory Model Analysis.
CoRR, 2020

2019
modelStudio: Interactive Studio with Explanations for ML Predictive Models.
J. Open Source Softw., 2019


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