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Agata Foryciarz
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2020 – today
- 2023
- [j2]Yizhe Xu, Agata Foryciarz, Ethan Steinberg, Nigam H. Shah
:
Clinical utility gains from incorporating comorbidity and geographic location information into risk estimation equations for atherosclerotic cardiovascular disease. J. Am. Medical Informatics Assoc. 30(5): 878-887 (2023) - 2022
- [c3]Stephen Pfohl
, Yizhe Xu, Agata Foryciarz, Nikolaos Ignatiadis, Julian Genkins, Nigam Shah:
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare. FAccT 2022: 1039-1052 - [i4]Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz, Nikolaos Ignatiadis, Julian Genkins, Nigam H. Shah:
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare. CoRR abs/2202.01906 (2022) - 2021
- [j1]Stephen R. Pfohl
, Agata Foryciarz, Nigam H. Shah:
An empirical characterization of fair machine learning for clinical risk prediction. J. Biomed. Informatics 113: 103621 (2021) - [i3]Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah:
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations. CoRR abs/2108.12250 (2021) - 2020
- [c2]Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, Monica S. Lam:
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking. ACL 2020: 122-132 - [c1]Katarzyna Szymielewicz, Anna Bacciarelli, Fanny Hidvegi, Agata Foryciarz, Soizic Pénicaud, Matthias Spielkamp:
Where do algorithmic accountability and explainability frameworks take us in the real world?: from theory to practice. FAT* 2020: 689 - [i2]Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, Monica S. Lam:
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking. CoRR abs/2005.00891 (2020) - [i1]Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah:
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction. CoRR abs/2007.10306 (2020)
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