7 papers
SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation
Shuo Yang, Zheyu Zhang, Bardh Prenkaj +1
Generating high-fidelity synthetic tabular data remains a critical challenge for enhancing data availability in privacy-sensitive and low-resource domains. Recent approaches levera…
TabSCM: A practical Framework for Generating Realistic Tabular Data
Sven Jacob, Bardh Prenkaj, Weijia Shao +1
Most tabular-data generators match marginal statistics yet ignore causal structure, leading downstream models to learn spurious or unfair patterns. We present TabSCM, a mixed-type…
Analysing the Safety Pitfalls of Steering Vectors
Yuxiao Li, Alina Fastowski, Efstratios Zaradoukas +2
Activation steering has emerged as a powerful tool to shape LLM behavior without the need for weight updates. While its inherent brittleness and unreliability are well-documented,…
Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks
Matteo De Sanctis, Riccardo De Sanctis, Stefano Faralli +2
Graph Neural Networks (GNNs) are increasingly adopted across domains such as molecular biology and social network analysis, yet their black-box nature hinders interpretability and…
A Multi-Agent Framework for Interpreting Multivariate Physiological Time Series
Davide Gabrielli, Paola Velardi, Stefano Faralli +1
Continuous physiological monitoring is central to emergency care, yet deploying trustworthy AI is challenging. While LLMs can translate complex physiological signals into clinical…
Moral Lenses, Political Coordinates: Towards Ideological Positioning of Morally Conditioned LLMs
Chenchen Yuan, Bolei Ma, Zheyu Zhang +3
While recent research has systematically documented political orientation in large language models (LLMs), existing evaluations rely primarily on direct probing or demographic pers…