26 citations · 45 across the 5 of their papers we have counts for
5 papers
Bayesian Quadrature for Neural Ensemble Search
Saad Hamid, Xingchen Wan, Martin Jørgensen +2
Ensembling can improve the performance of Neural Networks, but existing approaches struggle when the architecture likelihood surface has dispersed, narrow peaks. Furthermore, exist…
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels
Binxin Ru, Xingchen Wan, Xiaowen Dong +1
Current neural architecture search (NAS) strategies focus only on finding a single, good, architecture. They offer little insight into why a specific network is performing well, or…
A Maximum Entropy approach to Massive Graph Spectra
Diego Granziol, Robin Ru, Stefan Zohren +3
Graph spectral techniques for measuring graph similarity, or for learning the cluster number, require kernel smoothing. The choice of kernel function and bandwidth are typically ch…
A General Framework for Fair Regression
Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne +1
Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness c…
Entropic Spectral Learning for Large-Scale Graphs
Diego Granziol, Binxin Ru, Stefan Zohren +3
Graph spectra have been successfully used to classify network types, compute the similarity between graphs, and determine the number of communities in a network. For large graphs,…