Modular Autoencoders for Ensemble Feature Extraction. - Henry W. J. Reeve; Gavin Brown 001 (FE@NIPS, 2015)

Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours. - Henry W. J. Reeve; Gavin Brown 001 (ALT, 2017)

Degrees of Freedom in Regression Ensembles. - Henry W. J. Reeve; Gavin Brown 001 (ESANN, 2017)

The K-Nearest Neighbour UCB Algorithm for Multi-Armed Bandits with Covariates. - Henry W. J. Reeve; Joe Mellor; Gavin Brown 001 (ALT, 2018)

Modular Dimensionality Reduction. - Henry W. J. Reeve; Tingting Mu; Gavin Brown 001 (ECML/PKDD (1), 2018)

Diversity and degrees of freedom in regression ensembles. - Henry W. J. Reeve; Gavin Brown 001 (Neurocomputing, 2018)

Exploiting geometric structure in mixture proportion estimation with generalised Blanchard-Lee-Scott estimators. - Henry W. J. Reeve; Ata Kabán (ALT, 2019)

Classification with unknown class-conditional label noise on non-compact feature spaces. - Henry W. J. Reeve; Ata Kabán (COLT, 2019)

Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise. - Henry W. J. Reeve; Ata Kabán (ICML, 2019)

Joint Training of Neural Network Ensembles. - Andrew M. Webb; Charles Reynolds; Dan-Andrei Iliescu; Henry W. J. Reeve; Mikel Luján; Gavin Brown 001 (CoRR, 2019)

Learning in high dimensions with asymmetric costs. - Henry W. J. Reeve (2019)

Optimistic Bounds for Multi-output Learning. - Henry W. J. Reeve; Ata Kabán (ICML, 2020)

To Ensemble or Not Ensemble: When Does End-to-End Training Fail? - Andrew M. Webb; Charles Reynolds; Wenlin Chen; Henry W. J. Reeve; Dan-Andrei Iliescu; Mikel Luján; Gavin Brown 001 (ECML/PKDD (3), 2020)

Margin Maximization as Lossless Maximal Compression. - Nikolaos Nikolaou; Henry W. J. Reeve; Gavin Brown 001 (CoRR, 2020)

Optimistic bounds for multi-output prediction. - Henry W. J. Reeve; Ata Kabán (CoRR, 2020)

Statistical optimality conditions for compressive ensembles. - Henry W. J. Reeve; Ata Kabán (CoRR, 2021)

Adaptive transfer learning. - Henry W. J. Reeve; Timothy I. Cannings; Richard J. Samworth (CoRR, 2021)

Optimal subgroup selection. - Henry W. J. Reeve; Timothy I. Cannings; Richard J. Samworth (CoRR, 2021)

Density Ratio Estimation and Neyman Pearson Classification with Missing Data. - Josh Givens; Song Liu; Henry W. J. Reeve (AISTATS, 2023)

A Unified Theory of Diversity in Ensemble Learning. - Danny Wood; Tingting Mu; Andrew M. Webb; Henry W. J. Reeve; Mikel Luján; Gavin Brown 001 (J. Mach. Learn. Res., 2023)

Asymptotic Optimality for Decentralised Bandits. - Conor J. Newton; Ayalvadi Ganesh 001; Henry W. J. Reeve (Dyn. Games Appl., 2023)

Conditional Outcome Equivalence: A Quantile Alternative to CATE. - Josh Givens; Henry W. J. Reeve; Song Liu; Katarzyna Reluga (NeurIPS, 2024)

An adaptive transfer learning perspective on classification in non-stationary environments. - Henry W. J. Reeve (CoRR, 2024)

Heterogeneous sets in dimensionality reduction and ensemble learning. - Henry W. J. Reeve; Ata Kabán; Jakramate Bootkrajang (Mach. Learn., 2024)

Structure discovery in PAC-learning by random projections. - Ata Kabán; Henry W. J. Reeve (Mach. Learn., 2024)

Efficient learning with projected histograms. - Zhanliang Huang; Ata Kabán; Henry W. J. Reeve (Data Min. Knowl. Discov., 2024)

CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering. - Isabella Degen; Zahraa S. Abdallah; Henry W. J. Reeve; Kate Robson Brown (CoRR, 2025)

Score Matching With Missing Data. - Josh Givens; Song Liu; Henry W. J. Reeve (CoRR, 2025)

Canonical Correlation Patterns for Validating Clustering of Multivariate Time Series. - Isabella Degen; Zahraa S. Abdallah; Kate Robson Brown; Henry W. J. Reeve (CoRR, 2025)