Completed Student Projects
Completed Projects
Automatic Discovery of Trade-off Between Accuracy, Privacy and Fairness for ML models
When machine learning models are deployed to solve real world problems, they are often trained on sensitive data, e.g. healthcare or financial records. Practitioners need to ensure fairness and privacy of the resulting model. Often privacy and fairness guarantees may only be achieved through sacrificing accuracy (as classically measured). Usually both privacy and fairness are set as fixed constraints, and the exact effect of such constraints on accuracy is unclear. This project proposes to develop a procedure of automatic discovery of the trade-off between these three metrics.
Optimisation Benchmarks for Online RL
Reinforcement Learning algorithms have been applied in different domains. Now there is a growing interest in applying RL algorithms to optimisation problems. Such algorithms are a better alternative to produce near-optimal solutions in dynamic environments, compared against exact or approximation algorithms. Benchmarks are a key element in the development and evaluation of novel algorithms as they enable a standardised comparison of these algorithms’ performance. In this project you will provide a set of optimisation benchmarks to evaluate online RL algorithms.
Self-Adaptive Systems and Large Language Models (LLMs)
Software systems are increasingly complex and include different actors and components interacting in dynamic environments. Maintaining such systems is a difficult task where human intervention is not feasible. Autonomous computing has explored approaches to optimise systems’ performance by changing their structure, behaviour, or environment variables. These approaches rely on feedback loops that accumulate knowledge from the system interactions to inform autonomous decision-making. However, this knowledge is often limited, constraining the systems’ interpretability and adaptability. This project proposes to explore the capabilities of Large Language Models (LLMs) for self-adaptive systems. The main idea is to replace current autonomous RL-agents with LLM-based agents to make self-adaptive decisions.