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Alicja Gosiewska
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2020 – today
- 2023
- [j6]Alicja Gosiewska
, Zuzanna Baran, Monika Baran, Tomasz Rutkowski
:
Seeking a Sufficient Data Volume for Railway Infrastructure Component Detection with Computer Vision Models. Sensors 23(18): 7776 (2023) - 2022
- [j5]Michael Bücker
, Gero Szepannek
, Alicja Gosiewska
, Przemyslaw Biecek
:
Transparency, auditability, and explainability of machine learning models in credit scoring. J. Oper. Res. Soc. 73(1): 70-90 (2022) - [j4]Alicja Gosiewska
, Katarzyna Woznica
, Przemyslaw Biecek:
Interpretable meta-score for model performance. Nat. Mac. Intell. 4(9): 792-800 (2022) - [c2]Alicja Gosiewska, Katarzyna Woznica, Przemyslaw Biecek:
Interpretable Meta-Score for Model Performance: Extended Abstract. Meta-Knowledge Transfer @ ECML/PKDD 2022: 75-77 - 2021
- [j3]Alicja Gosiewska
, Anna Kozak
, Przemyslaw Biecek:
Simpler is better: Lifting interpretability-performance trade-off via automated feature engineering. Decis. Support Syst. 150: 113556 (2021) - [i9]Przemyslaw Biecek, Marcin Chlebus, Janusz Gajda, Alicja Gosiewska, Anna Kozak, Dominik Ogonowski, Jakub Sztachelski, Piotr Wojewnik:
Enabling Machine Learning Algorithms for Credit Scoring - Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models. CoRR abs/2104.06735 (2021) - 2020
- [i8]Alicja Gosiewska, Przemyslaw Biecek:
Lifting Interpretability-Performance Trade-off via Automated Feature Engineering. CoRR abs/2002.04267 (2020) - [i7]Alicja Gosiewska, Katarzyna Woznica, Przemyslaw Biecek:
Interpretable Meta-Measure for Model Performance. CoRR abs/2006.02293 (2020) - [i6]Szymon Maksymiuk
, Alicja Gosiewska, Przemyslaw Biecek:
Landscape of R packages for eXplainable Artificial Intelligence. CoRR abs/2009.13248 (2020) - [i5]Michael Bücker
, Gero Szepannek, Alicja Gosiewska, Przemyslaw Biecek:
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring. CoRR abs/2009.13384 (2020)
2010 – 2019
- 2019
- [j2]Alicja Gosiewska
, Przemyslaw Biecek:
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics. R J. 11(2): 85 (2019) - [c1]Barbara Rychalska, Dominika Basaj, Alicja Gosiewska
, Przemyslaw Biecek:
Models in the Wild: On Corruption Robustness of Neural NLP Systems. ICONIP (3) 2019: 235-247 - [i4]Alicja Gosiewska, Aleksandra Gacek, Piotr Lubon, Przemyslaw Biecek
:
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning. CoRR abs/1902.11035 (2019) - [i3]Alicja Gosiewska, Przemyslaw Biecek
:
iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models. CoRR abs/1903.11420 (2019) - [i2]Alicja Gosiewska, Mateusz Bakala, Katarzyna Woznica, Maciej Zwolinski, Przemyslaw Biecek:
EPP: interpretable score of model predictive power. CoRR abs/1908.09213 (2019) - 2018
- [j1]Aleksandra Grudziaz
, Alicja Gosiewska
, Przemyslaw Biecek
:
survxai: an R package for structure-agnostic explanations of survival models. J. Open Source Softw. 3(31): 961 (2018) - [i1]Alicja Gosiewska, Przemyslaw Biecek:
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic. CoRR abs/1809.07763 (2018)
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