625 citations · 721 across the 15 of their papers we have counts for
17 papers
Optimising for Energy Efficiency and Performance in Machine Learning
Emile Dos Santos Ferreira, Andrei Paleyes, Neil D. Lawrence
The ubiquity of machine learning (ML) and the demand for ever-larger models bring an increase in energy consumption and environmental impact. However, little is known about the ene…
Code Roulette: How Prompt Variability Affects LLM Code Generation
Andrei Paleyes, Radzim Sendyka, Diana Robinson +2
Code generation is one of the most active areas of application of Large Language Models (LLMs). While LLMs lower barriers to writing code and accelerate development process, the ov…
LLM Performance for Code Generation on Noisy Tasks
Radzim Sendyka, Christian Cabrera, Andrei Paleyes +2
This paper investigates the ability of large language models (LLMs) to recognise and solve tasks which have been obfuscated beyond recognition. Focusing on competitive programming…
Self-sustaining Software Systems (S4): Towards Improved Interpretability and Adaptation
Christian Cabrera, Andrei Paleyes, Neil D. Lawrence
Software systems impact society at different levels as they pervasively solve real-world problems. Modern software systems are often so sophisticated that their complexity exceeds…
Automated discovery of trade-off between utility, privacy and fairness in machine learning models
Bogdan Ficiu, Neil D. Lawrence, Andrei Paleyes
Machine learning models are deployed as a central component in decision making and policy operations with direct impact on individuals' lives. In order to act ethically and comply…
Causal fault localisation in dataflow systems
Andrei Paleyes, Neil D. Lawrence
Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-d…