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2020 – today
- 2024
[j3]Maryam Badar
, Marco Fisichella:
Fair-CMNB: Advancing Fairness-Aware Stream Learning with Naïve Bayes and Multi-Objective Optimization. Big Data Cogn. Comput. 8(2): 16 (2024)- [c4]Maryam Badar, Sandipan Sikdar, Wolfgang Nejdl, Marco Fisichella:
FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning. AAAI 2024: 10962-10970 - [c3]Maryam Badar, Sandipan Sikdar, Wolfgang Nejdl, Marco Fisichella:
TrustFed: Navigating Trade-offs Between Performance, Fairness, and Privacy in Federated Learning. ECAI 2024: 2370-2377 - 2023
- [j2]Maryam Badar, Wolfgang Nejdl, Marco Fisichella:
FAC-fed: Federated adaptation for fairness and concept drift aware stream classification. Mach. Learn. 112(8): 2761-2786 (2023) - [i2]Niloy Ganguly, Dren Fazlija, Maryam Badar, Marco Fisichella, Sandipan Sikdar, Johanna Schrader, Jonas Wallat, Koustav Rudra, Manolis Koubarakis, Gourab K. Patro, Wadhah Zai El Amri, Wolfgang Nejdl:
A Review of the Role of Causality in Developing Trustworthy AI Systems. CoRR abs/2302.06975 (2023) - 2022
- [i1]Maryam Badar, Marco Fisichella, Vasileios Iosifidis, Wolfgang Nejdl:
Discrimination and Class Imbalance Aware Online Naive Bayes. CoRR abs/2211.04812 (2022) - 2020
- [j1]Maryam Badar, Muhammad Haris, Anam Fatima:
Application of deep learning for retinal image analysis: A review. Comput. Sci. Rev. 35: 100203 (2020)
2010 – 2019
- 2018
- [c2]Maryam Badar, Muhammad Shahzad, M. M. Fraz:
Simultaneous Segmentation of Multiple Retinal Pathologies Using Fully Convolutional Deep Neural Network. MIUA 2018: 313-324 - [c1]Muhammad Haris, Imran Mahmood, Maryam Badar, Muhammad Saad Qaisar Alvi:
Modeling Safest and Optimal Emergency Evacuation Plan for Large-Scale Pedestrians Environments. WSC 2018: 917-928
Coauthor Index
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