2024

• Ramanaik Chethan Krishnamurthy, Arjun Roy, and Eirini Ntoutsi. Adversarial Robustness of VAEs across Intersectional Subgroups. 4th BIAS workshop, co-located with ECML PKDD 2024.
• Panagiotou Emmanouil, Arjun Roy, and Eirini Ntoutsi, Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study. 4th BIAS workshop, co-located with ECML PKDD 2024.
• Julia Huuk, Berend Denkena, Abheek Dhingra, Eirini Ntoutsi, Shape error prediction in 5-axis machining using graph neural networks. 18th CIRP ICME Conference on Intelligent Computation in Manufacturing Engineering. (accepted)
• Yi Cai, Arthur Zimek, Eirini Ntoutsi, Gerhard Wunder, Transparent Neighborhood Approximation for Text Classifier Explanation by Probability-based Editing. 11th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2024) (accepted)
• Swati Swati, Arjun Roy, Eirini Ntoutsi, Exploring Fusion Techniques in Multimodal AI-Based Recruitment: Insights from FairCVdb. Proceedings of the 2nd European Workshop on Algorithmic Fairness (EWAF’24) [local copy] (accepted).
• Vivek Kumar, Pushpraj Singh Rajawat, Giacomo Medda, Eirini Ntoutsi and Diego Reforgiato Recupero:  Unlocking LLMs Capabilities: Addressing Scarce Data and Inherent Bias Challenges in Mental Health and Therapeutic Counselling, International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security (NLPAICS 2024) (accepted).
• Qian, Han, Emmanouil Panagiotou, Mengyan Peng, Eirini Ntoutsi, Chongjie Kang, and Steffen Marx. "A novel dataset and feature selection for data-driven conceptual design of offshore jacket substructures." Ocean Engineering 303 (2024): 117679. [local copy].
• Arjun Roy, Christos Koutlis, Symeon Papadopoulos, Eirini Ntoutsi, FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning, International Joint Conference on Neural Networks (IJCNN)  (accepted).

• Siamak Ghodsi, Seyed Amjad Seyedi, Eirini Ntoutsi, Towards Cohesion-Fairness Harmony - Contrastive Regularization in Individual Fair Graph Clustering,  28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) [local copy].

2023

• Simone Fabbrizzi, Xuan Zhao, Emmanouil Krasanakis, Symeon Papadopoulos, Eirini Ntoutsi, Studying Bias in Visual Features through the Lens of Optimal Transport, 
  ECML-PKDD 2023 DAMI journal track [local copy].

• Vasilis Gkolemis, Theodore Dalamagas, Eirini Ntoutsi, Christos Diou, RHALE, Robust and Heterogeneity-aware Accumulated Local Effects,  26th European Conference on Artificial Intelligence (ECAI 2023) [local copy].

• Emmanouil Panagiotou, Eirini Ntoutsi, Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning, DynXAI workshop, co-located with ECML PKDD 2023 [local copy].

• Vasilis Gkolemis, Theodore Dalamagas, Eirini Ntoutsi, Christos Diou, Regionally Additive Models Explainable-by-design models minimizing feature interactions, XAI-uncertainty workshop, co-located with ECML PKDD 2023 [local copy].

• Siamak Ghodsi, Eirini Ntoutsi, Affinity Clustering Framework for Data Debiasing using Pairwise Distribution Discrepancy,  European Workshop on Algorithmic Fairness (EWAF’23), Winterthur, Switzerland.

• Emmanouil Panagiotou, Han Qian, Mareile Wynants, Anton Kriese, Steffen Marx, Eirini Ntoutsi, Explainable AI-based Generation of Offshore Substructure Designs, The 33rd International Ocean and Polar Engineering Conference (ISOPE-2023).

• Arjun Roy, Jan Horstmann, Eirini Ntoutsi, Multi-dimensional discrimination in Law and Machine Learning - A comparative overview,  ACM Conference on Fairness, Accountability, and Transparency 2023 (ACM FAccT 2023) [local copy] [video].

• Tai Le Quy, Gunnar Friege, Eirini Ntoutsi, Multi-fair capacitated students-topics grouping problem, The 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023)
  [local copy] [ppt] [video].

• Tai Le Quy, Gunnar Friege, Eirini Ntoutsi, A review of clustering models in educational data science towards fairness-aware learning,  Peña-Ayala, A. (eds) Educational Data Science: Essentials, Approaches, and Tendencies. Big Data Management. Springer, Singapore.

• Vasileios Iosifidis, Symeon Papadopoulos, Bodo Rosenhahn, Eirini Ntoutsi, AdaCC cumulative cost-sensitive boosting for imbalanced classification, Knowledge and Information Systems (KAIS)
  [local copy] [code].

2022

• Emmanouil Panagiotou, Multi-objective substructure generation, The 18th EAWE PhD Seminar.

• Yi Cai, Arthur Zimek, Gerhard Wunder, Eirini Ntoutsi, Power of Explanations - Towards automatic debiasing in hate speech detection,  9th IEEE International Conference on Data Science and Advanced Analytics (DSAA) [local copy] [code].

• Tai Le Quy, Thi Huyen Nguyen, Gunnar Friege, Eirini Ntoutsi, Evaluation of group fairness measures in student performance prediction problems, SoGood 2022 – 7th Workshop on Data Science for Social Good@ECML/PKDD 2022, [local copy] [code] [ppt] [video].

• Simone Fabrizzi, Symeon Papadopoulos, Eirini Ntoutsi, Yannis Kompatsiaris, A survey on bias in visual datasets,  Computer Vision and Image Understanding (CVIU) journal
  [local copy].

• Arjun Roy, Vasileios Iosifidis, Eirini Ntoutsi, Multi-fairness under class-imbalance, Discovery Science (DS)2022, [local copy] [video].

• Vasileios Iosifidis, Arjun Roy, Eirini Ntoutsi, Parity-based Cumulative Fairness-aware Boosting,  Knowledge and Information Systems (KAIS)
  [local copy] [code].

• Arjun Roy, Eirini Ntoutsi, Learning to Teach Fairness-aware Deep Multi-task Learning, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), 2022, [local copy] [code].

• Tai Le Quy, Arjun Roy, Vasileios Iosifidis, Wenbin Zhang, Eirini Ntoutsi, A survey on datasets for fairness-aware machine learning, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 2022, [local copy] [code].

Before 2022

• Eine Liste unserer bisher erschienenen Publikationen finden Sie hier.