9 citations · 10 across the 7 of their papers we have counts for
9 papers
FlowCF: Sparse Counterfactual Explanations for Mixed-Type Tabular Data using Flow Matching
Emmanouil Panagiotou, Eirini Ntoutsi
In the field of Explainable AI (XAI), counterfactual (CF) explanations interpret a model's decision by suggesting the changes to the input that would lead to a more favourable outc…
On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces
Chethan Krishnamurthy Ramanaik, Tobias Callies, Michael Hecht +1
Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry, feature robustness, input-output Jacobians, and the i…
TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees
Emmanouil Panagiotou, Benoît Ronval, Arjun Roy +4
Ensuring fairness in machine learning remains a significant challenge, as models often inherit biases from their training data. Generative models have recently emerged as a promisi…
MMM-fair: An Interactive Toolkit for Exploring and Operationalizing Multi-Fairness Trade-offs
Swati Swati, Arjun Roy, Emmanouil Panagiotou +1
Fairness-aware classification requires balancing performance and fairness, often intensified by intersectional biases. Conflicting fairness definitions further complicate the task,…
Achieving Hilbert-Schmidt Independence Under Rényi Differential Privacy for Fair and Private Data Generation
Tobias Hyrup, Emmanouil Panagiotou, Arjun Roy +3
As privacy regulations such as the GDPR and HIPAA and responsibility frameworks for artificial intelligence such as the AI Act gain traction, the ethical and responsible use of rea…
Revealing Hidden Vulnerabilities in Autoencoders through Gradient Signal Restoration
Chethan Krishnamurthy Ramanaik, Arjun Roy, Tobias Callies +1
Adversarial robustness of deep autoencoders (AEs) has received less attention than that of discriminative models, although their compressed latent representations induce ill-condit…
Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration
Vasiliki Papanikou, Danae Pla Karidi, Evaggelia Pitoura +2
As Artificial Intelligence (AI) is increasingly used in areas that significantly impact human lives, concerns about fairness and transparency have grown, especially regarding their…
TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE
Emmanouil Panagiotou, Manuel Heurich, Tim Landgraf +1
In the field of Explainable AI (XAI), counterfactual (CF) explanations are one prominent method to interpret a black-box model by suggesting changes to the input that would alter a…
Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
Emmanouil Panagiotou, Arjun Roy, Eirini Ntoutsi
Due to their data-driven nature, Machine Learning (ML) models are susceptible to bias inherited from data, especially in classification problems where class and group imbalances ar…