9 citations · 18 across the 6 of their papers we have counts for
12 papers
Asymptotic Optimality for Decentralised Bandits
Conor Newton, Ayalvadi Ganesh, Henry W. J. Reeve
We consider a large number of agents collaborating on a multi-armed bandit problem with a large number of arms. The goal is to minimise the regret of each agent in a communication-…
Adaptive transfer learning
Henry W. J. Reeve, Timothy I. Cannings, Richard J. Samworth
In transfer learning, we wish to make inference about a target population when we have access to data both from the distribution itself, and from a different but related source dis…
Statistical optimality conditions for compressive ensembles
Henry W. J. Reeve, Ata Kaban
We present a framework for the theoretical analysis of ensembles of low-complexity empirical risk minimisers trained on independent random compressions of high-dimensional data. Fi…
Optimistic bounds for multi-output prediction
Henry WJ Reeve, Ata Kaban
We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set. This includes a range of important proble…
Margin Maximization as Lossless Maximal Compression
Nikolaos Nikolaou, Henry Reeve, Gavin Brown
The ultimate goal of a supervised learning algorithm is to produce models constructed on the training data that can generalize well to new examples. In classification, functional m…
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise
Henry W. J. Reeve, Ata Kaban
We consider classification in the presence of class-dependent asymmetric label noise with unknown noise probabilities. In this setting, identifiability conditions are known, but ad…