5 papers
Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving
Yasmin Moslem, Magdalena Kacmajor, Vasudevan Nedumpozhimana +11
Serving open-weight language models on premises forces a trade-off between accuracy and computational cost. Hosting models of different sizes lets easy queries go to a small, effic…
Grammar-Guided Evolutionary Search for Discrete Prompt Optimisation
Muzhaffar Hazman, Minh-Khoi Pham, Shweta Soundararajan +10
Prompt engineering has proven to be a crucial step in leveraging pretrained large language models (LLMs) in solving various real-world tasks. Numerous solutions have been proposed…
UCB-driven Utility Function Search for Multi-objective Reinforcement Learning
Yucheng Shi, David Lynch, Alexandros Agapitos
In Multi-objective Reinforcement Learning (MORL) agents are tasked with optimising decision-making behaviours that trade-off between multiple, possibly conflicting, objectives. MOR…
Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation
Cengis Hasan, Alexandros Agapitos, David Lynch +4
We present a method that addresses the pain point of long lead-time required to deploy cell-level parameter optimisation policies to new wireless network sites. Given a sequence of…
Offline Contextual Bandits for Wireless Network Optimization
Miguel Suau, Alexandros Agapitos, David Lynch +3
The explosion in mobile data traffic together with the ever-increasing expectations for higher quality of service call for the development of AI algorithms for wireless network opt…