59 citations · 209 across the 50 of their papers we have counts for
5 papers · 1 filter
Optimal Stopping of Self-Refining Foundation Models
Kim Hammar, Tansu Alpcan, Emil C. Lupu
Foundation models can improve their outputs through a self-refinement process driven by external feedback. In this process, the model is embedded in an iterative loop where it gene…
When cheap gradients fail: the measurement cost of attacking quantum classifiers
Bacui Li, Chandra Thapa, Tansu Alpcan +1
Adversarial perturbations threaten machine learning classifiers, including variational quantum classifiers. We show that finite quantum measurement statistics (shot noise) act as a…
Parameter-efficient Quantum Multi-task Learning
Hevish Cowlessur, Chandra Thapa, Tansu Alpcan +1
Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely used hard-parameter-sharing se…
Causal Online Learning of Safe Regions in Cloud Radio Access Networks
Kim Hammar, Tansu Alpcan, Emil Lupu
Cloud radio access networks (RANs) enable cost-effective management of mobile networks by dynamically scaling their capacity on demand. However, deploying adaptive controllers to i…
Hallucination-Resistant Security Planning with a Large Language Model
Kim Hammar, Tansu Alpcan, Emil Lupu
Large language models (LLMs) are promising tools for supporting security management tasks, such as incident response planning. However, their unreliability and tendency to hallucin…