Projects funded by the NCN

Information on the principal investigator and host institution

Information of the project and the call

Keywords

Equipment

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HOMER: Human Oriented autoMated machinE leaRning

2019/34/E/ST6/00052

Keywords:

eXplainable Artificial Intelligence Automated Machine Learning Human-Centric Artificial Intelligence Hyperparameter tuning Interpretable Predictive Modeling

Descriptors:

  • ST6_007:
  • ST6_011:

Panel:

ST6 - Computer science and informatics: informatics and information systems, computer science, scientific computing, intelligent systems

Host institution :

Politechnika Warszawska, Wydział Matematyki i Nauk Informacyjnych

woj. mazowieckie

Other projects carried out by the institution 

Principal investigator (from the host institution):

dr hab. Przemysław Biecek 

Number of co-investigators in the project: 7

Call: SONATA BIS 9 - announced on 2019-06-17

Amount awarded: 2 043 400 PLN

Project start date (Y-m-d): 2020-04-15

Project end date (Y-m-d): 2026-04-14

Project duration:: 72 months (the same as in the proposal)

Project status: Pending project

Project description

Download the project description in a pdf file

Note - project descriptions were prepared by the authors of the applications themselves and placed in the system in an unchanged form.

Information in the final report

  • Publication in academic press/journals (13)
  • Articles in post-conference publications (8)
  1. Performance Is Not Enough: The Story Told by a Rashomon Quartet
    Authors:
    Przemysław Biecek,Hubert, Mateusz Krzyziński, Dianne Cook
    Academic press:
    Journal of Computational and Graphical Statistics (rok: 2024, tom: 33, strony: 1118-1121), Wydawca: Taylor & Francs
    Status:
    Published
    DOI:
    10.1080/10618600.2024.2344616 - link to the publication
  2. Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data
    Authors:
    Katarzyna Kobylińska; Mateusz Krzyziński; Rafał Machowicz; Mariusz Adamek; Przemysław Biecek
    Academic press:
    IEEE Journal of Biomedical and Health Informatics (rok: 2024, tom: 28, strony: 6454 - 6465), Wydawca: IEEE
    Status:
    Published
    DOI:
    10.1109/JBHI.2024.3443069 - link to the publication
  3. Explainable Machine Learning for Lung Cancer Screening Models
    Authors:
    Katarzyna Kobylińska,Tadeusz Orłowski, Mariusz Adamek, Przemysław Biecek
    Academic press:
    Applied Sciences (rok: 2022, tom: 12, strony: 1926), Wydawca: MDPI
    Status:
    Published
    DOI:
    10.3390/app12041926 - link to the publication
  4. SurvSHAP(t): Time-dependent explanations of machine learning survival models
    Authors:
    Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki, Przemysław Biecek
    Academic press:
    Knowledge-Based Systems (rok: 2023, tom: 262, strony: 45670), Wydawca: Elsevier
    Status:
    Published
    DOI:
    10.1016/j.knosys.2022.110234 - link to the publication
  5. Multi-omics disease module detection with an explainable Greedy Decision Forest
    Authors:
    Bastian Pfeifer, Hubert Baniecki, Anna Saranti, Przemyslaw Biecek, Andreas Holzinger
    Academic press:
    Scientific Reports (rok: 2022, tom: 12, strony: 16857), Wydawca: Nature
    Status:
    Published
    DOI:
    10.1038/s41598-022-21417-8 - link to the publication
  6. LIMEcraft: handcrafted superpixel selection and inspection for Visual eXplanations
    Authors:
    Weronika Hryniewska, Adrianna Grudzień, Przemysław Biecek
    Academic press:
    Machine Learning (rok: 2022, tom: 1, strony: 1), Wydawca: Springer
    Status:
    Published
    DOI:
    10.1007/s10994-022-06204-w - link to the publication
  7. Explainable Machine Learning for Lung Cancer Screening Models
    Authors:
    Katarzyna Kobylińska,Tadeusz Orłowski, Mariusz Adamek, Przemysław Biecek
    Academic press:
    Applied Sciences (rok: 2022, tom: 12, strony: 1926), Wydawca: MDPI
    Status:
    Published
    DOI:
    10.3390/app12041926 - link to the publication
  8. fairmodels: a Flexible Tool for Bias Detection, Visualization, and Mitigation in Binary Classification Models
    Authors:
    Jakub Wiśniewski, Przemyslaw Biecek
    Academic press:
    R Journal (rok: 2022, tom: 14/1, strony: 227 - 243), Wydawca: R Foundation
    Status:
    Published
    DOI:
    10.32614/RJ-2022-019 - link to the publication
  9. Consolidated learning: a domain‑specifc model‑free optimization strategy with validation on metaMIMIC benchmarks
    Authors:
    Katarzyna Woźnica, Mateusz Grzyb, Zuzanna Trafas, Przemysław Biecek
    Academic press:
    Machine Learning (rok: 2023, tom: 1, strony: 45677), Wydawca: Springer
    Status:
    Published
    DOI:
    10.1007/s10994-023-06359-0 - link to the publication
  10. The grammar of interactive explanatory model analysis
    Authors:
    Hubert Baniecki, Dariusz Parzych, Przemyslaw Biecek
    Academic press:
    Data Mining and Knowledge Discovery (rok: 2023, tom: 1, strony: 13516), Wydawca: Springer
    Status:
    Published
    DOI:
    link to the publication
  11. Consolidated learning: a domain‑specifc model‑free optimization strategy with validation on metaMIMIC benchmarks
    Authors:
    Katarzyna Woźnica, Mateusz Grzyb, Zuzanna Trafas, Przemysław Biecek
    Academic press:
    Machine Learning (rok: 2023, tom: 1, strony: 45677), Wydawca: Springer
    Status:
    Published
    DOI:
    10.1007/s10994-023-06359-0 - link to the publication
  12. LIMEcraft: handcrafted superpixel selection and inspection for Visual eXplanations
    Authors:
    Weronika Hryniewska, Adrianna Grudzień, Przemysław Biecek
    Academic press:
    Machine Learning (rok: 2022, tom: 1, strony: 1), Wydawca: Springer
    Status:
    Published
    DOI:
    10.1007/s10994-022-06204-w - link to the publication
  13. survex: an R package for explaining machine learning survival models
    Authors:
    Mikołaj Spytek, Mateusz Krzyziński, Sophie Hanna Langbein, Hubert Baniecki, Marvin N Wright, Przemysław Biecek
    Academic press:
    Bioinformatics (rok: 2023, tom: 12, strony: 45659), Wydawca: Oxford academic
    Status:
    Published
    DOI:
    10.1093/bioinformatics/btad723 - link to the publication
  1. Resistance Against Manipulative AI: Key Factors and Possible Actions
    Authors:
    Piotr Wilczyński, Wiktoria Mieleszczenko-Kowszewicz, Przemysław Biecek
    Conference:
    Frontiers in Artificial Intelligence and Applications (rok: 2024, tom: European Conference on Artificial Intelligence, strony: 802 - 809), Wydawca: IOS Press
    Data:
    konferencja 45547
    Status:
    Published
    DOI:
    10.3233/FAIA240565 - link to the publication
  2. Explain to Question not to Justify
    Authors:
    Przemyslaw Biecek, Wojciech Samek
    Conference:
    Proceedings of the 41st International Conference on Machine Learning (rok: 2024, tom: International Conference on Machine Learning, strony: 3996-4006), Wydawca: PMLR
    Data:
    konferencja 45494
    Status:
    Published
    DOI:
    link to the publication
  3. The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems
    Authors:
    Adrian Stando, Mustafa Cavus, Przemyslaw Biecek
    Conference:
    Proceedings of the Fifth International Workshop on Learning with Imbalanced Domains: Theory and Applications (rok: 2024, tom: ECML Workshop on Learning with Imbalanced Domains: Theory and Applications, strony: 16-30), Wydawca: PMLR
    Data:
    konferencja 45546
    Status:
    Published
    DOI:
    link to the publication
  4. Explainable expected goal models for performance analysis in football analytics
    Authors:
    Mustafa Cavus; Przemysław Biecek
    Conference:
    IEEE eXplore: International Conference on Data Science and Advanced Analytics (DSAA) (rok: 2023, tom: International Conference on Data Science and Advanced Analytics (DSAA), strony: 45666), Wydawca: IEEE eXplore
    Data:
    konferencja 44847
    Status:
    Published
    DOI:
    10.1109/DSAA54385.2022.10032440 - link to the publication
  5. Red‐Teaming Segment Anything Model
    Authors:
    Krzysztof Jankowski, Bartlomiej Sobieski, Mateusz Kwiatkowski, Jakub Szulc, Michał Janik, Hubert Baniecki, Przemysław Biecek
    Conference:
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (rok: 2024, tom: Workshops at Conference on Computer Vision and Pattern Recognition, strony: 2947-2956), Wydawca: PMLR
    Data:
    konferencja 45516
    Status:
    Published
    DOI:
    link to the publication
  6. Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)
    Authors:
    Hubert Baniecki, Przemyslaw Biecek
    Conference:
    IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations (rok: 2022, tom: AAAI Conference on Artificial Intelligence, strony: 12907-12908), Wydawca: AAAI
    Data:
    konferencja 44740
    Status:
    Published
    DOI:
    10.1609/aaai.v36i11.21590 - link to the publication
  7. Manipulating SHAP via Adversarial Data Perturbations (Student Abstract)
    Authors:
    Hubert Baniecki, Przemyslaw Biecek
    Conference:
    IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations (rok: 2022, tom: AAAI Conference on Artificial Intelligence, strony: 12907-12908), Wydawca: AAAI
    Data:
    konferencja 44740
    Status:
    Published
    DOI:
    10.1609/aaai.v36i11.21590 - link to the publication
  8. Global Counterfactual Directions
    Authors:
    Bartlomiej Sobieski, Przemyslaw Biecek
    Conference:
    European Conference on Computer Vision (rok: 2024, tom: European Conference on Computer Vision, strony: 72–90), Wydawca: Springer
    Data:
    konferencja 45547
    Status:
    Published
    DOI:
    10.1007/978-3-031-73036-8_5 - link to the publication

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