56 citations · 68 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Desiderata for next generation of ML model serving
Sherif Akoush, Andrei Paleyes, Arnaud Van Looveren +1
Inference is a significant part of ML software infrastructure. Despite the variety of inference frameworks available, the field as a whole can be considered in its early days. This…
Emulation of physical processes with Emukit
Andrei Paleyes, Mark Pullin, Maren Mahsereci +3
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical…
Good practices for Bayesian Optimization of high dimensional structured spaces
Eero Siivola, Javier Gonzalez, Andrei Paleyes +1
The increasing availability of structured but high dimensional data has opened new opportunities for optimization. One emerging and promising avenue is the exploration of unsupervi…