These are some projects being run for Cambridge Computer Lab students by the ML@CL group.

Optimisation Benchmarks for Online RL

Supervisor:   Pierre Thodoroff, Christian Cabrera, Neil D. Lawrence

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.

Automatic discovery of trade-off between accuracy, privacy and fairness for ML models

Supervisor:   Andrei Paleyes, Neil D. Lawrence

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.