The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods. - Gavin Brown 001; Jeremy L. Wyatt (ICML, 2003)

Negative Correlation Learning and the Ambiguity Family of Ensemble Methods. - Gavin Brown 001; Jeremy L. Wyatt (Multiple Classifier Systems, 2003)

Diversity in neural network ensembles. - Gavin Brown 001 (2004)

Between Two Extremes: Examining Decompositions of the Ensemble Objective Function. - Gavin Brown 001; Jeremy L. Wyatt; Ping Sun (Multiple Classifier Systems, 2005)

Diversity creation methods: a survey and categorisation. - Gavin Brown 001; Jeremy L. Wyatt; Rachel Harris; Xin Yao 001 (Inf. Fusion, 2005)

Managing Diversity in Regression Ensembles. - Gavin Brown 001; Jeremy L. Wyatt; Peter Tiño (J. Mach. Learn. Res., 2005)

Return Value Prediction meets Information Theory. - Jeremy Singer; Gavin Brown 001 (QAPL, 2006)

UCSpv: principled voting in UCS rule populations. - Gavin Brown 001; Tim Kovacs; James A. R. Marshall (GECCO, 2007)

Bayesian estimation of rule accuracy in UCS. - James A. R. Marshall; Gavin Brown 001; Tim Kovacs (GECCO (Companion), 2007)

Intelligent selection of application-specific garbage collectors. - Jeremy Singer; Gavin Brown 001; Ian Watson; John Cavazos (ISMM, 2007)

Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation. - Manuela Zanda; Gavin Brown 001; Giorgio Fumera; Fabio Roli (MCS, 2007)

Towards intelligent analysis techniques for object pretenuring. - Jeremy Singer; Gavin Brown 001; Mikel Luján; Ian Watson (PPPJ, 2007)

Sparse Distributed Memory Using Rank-Order Neural Codes. - Stephen B. Furber; Gavin Brown 001; Joy Bose; J. Mike Cumpstey; P. Marshall; Jonathan L. Shapiro (IEEE Trans. Neural Networks, 2007)

Modeling UCS as a mixture of experts. - Narayanan Unny Edakunni; Tim Kovacs; Gavin Brown 001; James A. R. Marshall (GECCO, 2009)

Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical Data. - Amir Ahmad; Gavin Brown 001 (MCS, 2009)

A Study of Random Linear Oracle Ensembles. - Amir Ahmad; Gavin Brown 001 (MCS, 2009)

An Information Theoretic Perspective on Multiple Classifier Systems. - Gavin Brown 001 (MCS, 2009)

A Study of Semi-supervised Generative Ensembles. - Manuela Zanda; Gavin Brown 001 (MCS, 2009)

Fundamental Nano-Patterns to Characterize and Classify Java Methods. - Jeremy Singer; Gavin Brown 001; Mikel Luján; Adam Craig Pocock; Paraskevas Yiapanis (LDTA, 2009)

A New Perspective for Information Theoretic Feature Selection. - Gavin Brown 001 (AISTATS, 2009)

Analytic Solutions to Differential Equations under Graph-Based Genetic Programming. - Tom Seaton; Gavin Brown 001; Julian F. Miller (EuroGP, 2010)

Toward a more accurate understanding of the limits of the TLS execution paradigm. - Nikolas Ioannou; Jeremy Singer; Salman Khan 002; Polychronis Xekalakis; Paraskevas Yiapanis; Adam Craig Pocock; Gavin Brown 001; Mikel Luján; Ian Watson; Marcelo Cintra (IISWC, 2010)

The economics of garbage collection. - Jeremy Singer; Richard E. Jones; Gavin Brown 001; Mikel Luján (ISMM, 2010)

Some Thoughts at the Interface of Ensemble Methods and Feature Selection. - Gavin Brown 001 (MCS, 2010)

"Good" and "Bad" Diversity in Majority Vote Ensembles. - Gavin Brown 001; Ludmila I. Kuncheva (MCS, 2010)

Online Non-stationary Boosting. - Adam Craig Pocock; Paraskevas Yiapanis; Jeremy Singer; Mikel Luján; Gavin Brown 001 (MCS, 2010)

Learn++.MF: A random subspace approach for the missing feature problem. - Robi Polikar; Joseph DePasquale; Hussein Syed Mohammed; Gavin Brown 001; Ludmila I. Kuncheva (Pattern Recognit., 2010)

Ensemble Learning. - Gavin Brown 001 (Encyclopedia of Machine Learning, 2010)

Theoretical and empirical analysis of diversity in non-stationary learning. - Richard John Stapenhurst; Gavin Brown 001 (CIDUE, 2011)

Online, GA based mixture of experts: a probabilistic model of ucs. - Narayanan Unny Edakunni; Gavin Brown 001; Tim Kovacs (GECCO, 2011)

Accuracy exponentiation in UCS and its effect on voting margins. - Tim Kovacs; Narayanan Unny Edakunni; Gavin Brown 001 (GECCO, 2011)

Garbage collection auto-tuning for Java mapreduce on multi-cores. - Jeremy Singer; George Kovoor; Gavin Brown 001; Mikel Luján (ISMM, 2011)

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection. - Gavin Brown 001; Adam Craig Pocock; Ming-Jie Zhao; Mikel Luján (J. Mach. Learn. Res., 2012)

Informative Priors for Markov Blanket Discovery. - Adam Craig Pocock; Mikel Luján; Gavin Brown 001 (AISTATS, 2012)

Exploring sketches for probability estimation with sublinear memory. - Anthony Kleerekoper; Mikel Luján; Gavin Brown 001 (IEEE BigData, 2013)

ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge. - Michele Filannino; Gavin Brown 001; Goran Nenadic (SemEval@NAACL-HLT, 2013)

Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications. - Ming-Jie Zhao; Narayanan Unny Edakunni; Adam Craig Pocock; Gavin Brown 001 (J. Mach. Learn. Res., 2013)

Optimizing software runtime systems for speculative parallelization. - Paraskevas Yiapanis; Demian Rosas-Ham; Gavin Brown 001; Mikel Luján (ACM Trans. Archit. Code Optim., 2013)

Predicting OWL Reasoners: Locally or Globally? - Viachaslau Sazonau; Uli Sattler; Gavin Brown 001 (Description Logics, 2014)

Predicting Performance of OWL Reasoners: Locally or Globally? - Viachaslau Sazonau; Uli Sattler; Gavin Brown 001 (KR, 2014)

Statistical Hypothesis Testing in Positive Unlabelled Data. - Konstantinos Sechidis; Borja Calvo; Gavin Brown 001 (ECML/PKDD (3), 2014)

Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood. - Konstantinos Sechidis; Nikolaos Nikolaou; Gavin Brown 001 (S+SSPR, 2014)

Random Projection Random Discretization Ensembles - Ensembles of Linear Multivariate Decision Trees. - Amir Ahmad; Gavin Brown 001 (IEEE Trans. Knowl. Data Eng., 2014)

A scalable implementation of information theoretic feature selection for high dimensional data. - Anthony Kleerekoper; Michael Pappas; Adam Craig Pocock; Gavin Brown 001; Mikel Luján (IEEE BigData, 2015)

Is Feature Selection Secure against Training Data Poisoning? - Huang Xiao; Battista Biggio; Gavin Brown 001; Giorgio Fumera; Claudia Eckert 001; Fabio Roli (ICML, 2015)

Calibrating AdaBoost for Asymmetric Learning. - Nikolaos Nikolaou; Gavin Brown 001 (MCS, 2015)

Measuring the Stability of Feature Selection with Applications to Ensemble Methods. - Sarah Nogueira; Gavin Brown 001 (MCS, 2015)

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

General Terminology Induction in OWL. - Viachaslau Sazonau; Uli Sattler; Gavin Brown 001 (OWLED, 2015)

Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data. - Konstantinos Sechidis; Gavin Brown 001 (ECML/PKDD (1), 2015)

Random Ordinality Ensembles: Ensemble methods for multi-valued categorical data. - Amir Ahmad; Gavin Brown 001 (Inf. Sci., 2015)

On unifiers, diversifiers, and the nature of pattern recognition. - Gavin Brown 001 (Pattern Recognit. Lett., 2015)

Estimating Mutual Information in Under-Reported Variables. - Konstantinos Sechidis; Matthew Sperrin; Emily Petherick; Gavin Brown 001 (Probabilistic Graphical Models, 2016)

Measuring the Stability of Feature Selection. - Sarah Nogueira; Gavin Brown 001 (ECML/PKDD (2), 2016)

Ranking Biomarkers Through Mutual Information. - Konstantinos Sechidis; Emily Turner; Paul D. Metcalfe; James Weatherall; Gavin Brown 001 (CoRR, 2016)

Cost-sensitive boosting algorithms: Do we really need them? - Nikolaos Nikolaou; Narayanan Unny Edakunni; Meelis Kull; Peter A. Flach; Gavin Brown 001 (Mach. Learn., 2016)

Compiler-Driven Software Speculation for Thread-Level Parallelism. - Paraskevas Yiapanis; Gavin Brown 001; Mikel Luján (ACM Trans. Program. Lang. Syst., 2016)

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

Boosting Java Performance Using GPGPUs. - James Clarkson; Christos Kotselidis; Gavin Brown 001; Mikel Luján (ARCS, 2017)

Mutual information for improving the efficiency of the SCH algorithm. - Diego Fernández-Francos; Oscar Fontenla-Romero; Amparo Alonso-Betanzos; Gavin Brown 001 (ESANN, 2017)

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

On the Use of Spearman's Rho to Measure the Stability of Feature Rankings. - Sarah Nogueira; Konstantinos Sechidis; Gavin Brown 001 (IbPRIA, 2017)

Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid. - Marco Melis; Ambra Demontis; Battista Biggio; Gavin Brown 001; Giorgio Fumera; Fabio Roli (ICCV Workshops, 2017)

Exploring the consequences of distributed feature selection in DNA microarray data. - Verónica Bolón-Canedo; Konstantinos Sechidis; Noelia Sánchez-Maroño; Amparo Alonso-Betanzos; Gavin Brown 001 (IJCNN, 2017)

Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions. - Nikolaos Nikolaou; Efstratios Batzelis; Gavin Brown 001 (DARE@PKDD/ECML, 2017)

Dealing with under-reported variables: An information theoretic solution. - Konstantinos Sechidis; Matthew Sperrin; Emily Petherick; Mikel Luján; Gavin Brown 001 (Int. J. Approx. Reason., 2017)

On the Stability of Feature Selection Algorithms. - Sarah Nogueira; Konstantinos Sechidis; Gavin Brown 001 (J. Mach. Learn. Res., 2017)

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

Toward an Understanding of Adversarial Examples in Clinical Trials. - Konstantinos Papangelou; Konstantinos Sechidis; James Weatherall; Gavin Brown 001 (ECML/PKDD (1), 2018)

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

Distinguishing prognostic and predictive biomarkers: an information theoretic approach. - Konstantinos Sechidis; Konstantinos Papangelou; Paul D. Metcalfe; David Svensson; James Weatherall; Gavin Brown 001 (Bioinform., 2018)

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

Simple strategies for semi-supervised feature selection. - Konstantinos Sechidis; Gavin Brown 001 (Mach. Learn., 2018)

On the Stability of Feature Selection in the Presence of Feature Correlations. - Konstantinos Sechidis; Konstantinos Papangelou; Sarah Nogueira; James Weatherall; Gavin Brown 001 (ECML/PKDD (1), 2019)

ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM. - Andrew M. Webb; Gavin Brown 001; Mikel Luján (TAROS (1), 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)

Hybrid extreme learning machine approach for heterogeneous neural networks. - Vasileios Christou; Markos G. Tsipouras; Nikolaos Giannakeas; Alexandros T. Tzallas; Gavin Brown 001 (Neurocomputing, 2019)

Insights into distributed feature ranking. - Verónica Bolón-Canedo; Konstantinos Sechidis; Noelia Sánchez-Maroño; Amparo Alonso-Betanzos; Gavin Brown 001 (Inf. Sci., 2019)

Efficient feature selection using shrinkage estimators. - Konstantinos Sechidis; Laura Azzimonti; Adam Craig Pocock; Giorgio Corani; James Weatherall; Gavin Brown 001 (Mach. Learn., 2019)

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)

Better Boosting with Bandits for Online Learning. - Nikolaos Nikolaou; Joseph C. Mellor; Nikunj C. Oza; Gavin Brown 001 (CoRR, 2020)

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

Feature selection with limited bit depth mutual information for portable embedded systems. - Laura Morán-Fernández; Konstantinos Sechidis; Verónica Bolón-Canedo; Amparo Alonso-Betanzos; Gavin Brown 001 (Knowl. Based Syst., 2020)

Correction to: Efficient feature selection using shrinkage estimators. - Konstantinos Sechidis; Laura Azzimonti; Adam Craig Pocock; Giorgio Corani; James Weatherall; Gavin Brown 001 (Mach. Learn., 2020)

EnnCore: End-to-End Conceptual Guarding of Neural Architectures. - Edoardo Manino; Danilo S. Carvalho; Yi Dong 002; Julia Rozanova; Xidan Song; Mustafa A. Mustafa; André Freitas; Gavin Brown 001; Mikel Luján; Xiaowei Huang 001; Lucas C. Cordeiro (SafeAI@AAAI, 2022)

Bias-Variance Decompositions for Margin Losses. - Danny Wood; Tingting Mu; Gavin Brown 001 (AISTATS, 2022)

Outlier detection of vital sign trajectories from COVID-19 patients. - Sara Summerton; Ann Tivey; Rohan Shotton; Gavin Brown 001; Oliver C. Redfern; Rachel Oakley; John Radford; David C. Wong 004 (EMBC, 2023)

Tiny Classifier Circuits: Evolving Accelerators for Tabular Data. - Konstantinos Iordanou; Timothy Atkinson 001; Emre Ozer 001; Jedrzej Kufel; John Biggs; Gavin Brown 001; Mikel Luján (CoRR, 2023)

A max-affine spline approximation of neural networks using the Legendre transform of a convex-concave representation. - Adam Perrett; Danny Wood; Gavin Brown 001 (CoRR, 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)

FINESSD: Near-Storage Feature Selection with Mutual Information for Resource-Limited FPGAs. - Nikolaos Kyparissas; Gavin Brown 001; Mikel Luján (FCCM, 2024)

Bias/Variance is not the same as Approximation/Estimation. - Gavin Brown 001; Riccardo Ali (Trans. Mach. Learn. Res., 2024)