From the 1 of 9 linked papers with an AI index.
9 papers
Beyond Binary Rewards: A Comparative Study of Reward Design for Reinforcement Unlearning
Efstratios Zaradoukas, Davide Gabrielli, Bardh Prenkaj +1
The paper investigates how different reward functions affect the speed and effectiveness of reinforcement‑learning based machine unlearning for language models, proposing graded an…
Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
Zheyu Zhang, Shuo Yang, Bardh Prenkaj +1
Generative tabular augmentation is appealing in data-scarce domains, yet the prevailing focus on distributional fidelity does not reliably translate into better downstream models.…
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,…
Reinforcement Unlearning via Group Relative Policy Optimization
Efstratios Zaradoukas, Bardh Prenkaj, Gjergji Kasneci
During pretraining, LLMs inadvertently memorize sensitive or copyrighted data, posing significant compliance challenges under legal frameworks like the GDPR and the EU AI Act. Fulf…