6 papers · 1 filter
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…
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…
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…
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…