Andrei Paleyes — Projects to Supervise

Automatic Discovery of Trade-off Between Accuracy, Privacy and Fairness for ML models

Supervisors: 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.

Automatic Discovery of Trade-off Between Utility and Energy Comsumption of ML models

Supervisors: Andrei Paleyes, Neil D. Lawrence

The use of machine learning models both in academia and industry is on the rise. And so is the environmental impact of ML models. While deploying a model to production, it is important to be able to estimate its carbon footprint and to understand the costs involved in running it. Balancing performance and carbon efficiency of ML models becomes critical to ensure that the benefits of ML are maximized while minimizing its environmental costs. This project proposes to develop a procedure that automatically discovers and quantifies this trade-off.

Multi-objective optimisation of cloud infrastructure

Supervisors: Andrei Paleyes, Neil D. Lawrence

Machine learning (ML) and optimisation techniques are increasingly used to help solve decision-making problems that would be difficult or time-consuming to address manually. One such problem is the configuration of cloud infrastructure, where many deployment parameters can affect several competing objectives at the same time. This project investigates the use of multi-objective optimisation to automatically explore different cloud infrastructure configurations defined through Infrastructure-as-Code templates. Our aim will be to build a fully automated system that identifies a range of Pareto-optimal configurations that represent different trade-offs between the objectives being considered. Such a system can help reduce the time and cost required to create efficient cloud deployments while providing a better understanding of the available configuration choices.