Gavin Brown

ORCID

Publication Activity (10 Years)

Years Active: 2003-2023
Publications (10 Years): 54

Top Venues

University of Birmingham, UK (PHD)

Education History

University of Birmingham, UK

PHD

Publications

Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Luján, Gavin Brown
A Unified Theory of Diversity in Ensemble Learning. CoRR (2023)
Adam Perrett, Danny Wood, Gavin Brown
A max-affine spline approximation of neural networks using the Legendre transform of a convex-concave representation. CoRR (2023)
Danny Wood, Tingting Mu, Gavin Brown
Bias-Variance Decompositions for Margin Losses. AISTATS (2022)
Danny Wood, Tingting Mu, Gavin Brown
Bias-Variance Decompositions for Margin Losses. CoRR (2022)
Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown
Correction to: Efficient feature selection using shrinkage estimators. Mach. Learn. 109 (8) (2020)
Laura Morán-Fernández, Konstantinos Sechidis, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, Gavin Brown
Feature selection with limited bit depth mutual information for portable embedded systems. Knowl. Based Syst. 197 (2020)
Nikolaos Nikolaou, Henry W. J. Reeve, Gavin Brown
Margin Maximization as Lossless Maximal Compression. CoRR (2020)
Nikolaos Nikolaou, Joseph C. Mellor, Nikunj C. Oza, Gavin Brown
Better Boosting with Bandits for Online Learning. CoRR (2020)
Andrew M. Webb, Charles Reynolds, Wenlin Chen, Henry W. J. Reeve, Dan-Andrei Iliescu, Mikel Luján, Gavin Brown
To Ensemble or Not Ensemble: When Does End-to-End Training Fail? ECML/PKDD (3) (2020)
Verónica Bolón-Canedo, Konstantinos Sechidis, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, Gavin Brown
Insights into distributed feature ranking. Inf. Sci. 496 (2019)
Andrew M. Webb, Gavin Brown, Mikel Luján
ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM. TAROS (1) (2019)
Andrew M. Webb, Charles Reynolds, Dan-Andrei Iliescu, Henry W. J. Reeve, Mikel Luján, Gavin Brown
Joint Training of Neural Network Ensembles. CoRR (2019)
Vasileios Christou, Markos G. Tsipouras, Nikolaos Giannakeas, Alexandros T. Tzallas, Gavin Brown
Hybrid extreme learning machine approach for heterogeneous neural networks. Neurocomputing 361 (2019)
Konstantinos Sechidis, Konstantinos Papangelou, Sarah Nogueira, James Weatherall, Gavin Brown
On the Stability of Feature Selection in the Presence of Feature Correlations. ECML/PKDD (1) (2019)
Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown
Efficient feature selection using shrinkage estimators. Mach. Learn. 108 (8-9) (2019)
Konstantinos Sechidis, Konstantinos Papangelou, Paul D. Metcalfe, David Svensson, James Weatherall, Gavin Brown
Distinguishing prognostic and predictive biomarkers: an information theoretic approach. Bioinform. 34 (23) (2018)
Henry W. J. Reeve, Gavin Brown
Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours. CoRR (2018)
Konstantinos Papangelou, Konstantinos Sechidis, James Weatherall, Gavin Brown
Toward an Understanding of Adversarial Examples in Clinical Trials. ECML/PKDD (1) (2018)
Henry W. J. Reeve, Joe Mellor, Gavin Brown
The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates. CoRR (2018)
Henry W. J. Reeve, Gavin Brown
Diversity and degrees of freedom in regression ensembles. CoRR (2018)
Henry W. J. Reeve, Gavin Brown
Diversity and degrees of freedom in regression ensembles. Neurocomputing 298 (2018)
Konstantinos Sechidis, Konstantinos Papangelou, Paul D. Metcalfe, David Svensson, James Weatherall, Gavin Brown
Distinguishing prognostic and predictive biomarkers: an information theoretic approach. Bioinform. 34 (19) (2018)
Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli
Is feature selection secure against training data poisoning? CoRR (2018)
Henry W. J. Reeve, Joe Mellor, Gavin Brown
The K-Nearest Neighbour UCB Algorithm for Multi-Armed Bandits with Covariates. ALT (2018)
Konstantinos Sechidis, Gavin Brown
Simple strategies for semi-supervised feature selection. Mach. Learn. 107 (2) (2018)
Henry W. J. Reeve, Tingting Mu, Gavin Brown
Modular Dimensionality Reduction. ECML/PKDD (1) (2018)
Diego Fernández-Francos, Oscar Fontenla-Romero, Amparo Alonso-Betanzos, Gavin Brown
Mutual information for improving the efficiency of the SCH algorithm. ESANN (2017)
Henry W. J. Reeve, Gavin Brown
Degrees of Freedom in Regression Ensembles. ESANN (2017)
Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Mikel Luján, Gavin Brown
Dealing with under-reported variables: An information theoretic solution. Int. J. Approx. Reason. 85 (2017)
Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, Fabio Roli
Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid. ICCV Workshops (2017)
Sarah Nogueira, Konstantinos Sechidis, Gavin Brown
On the Stability of Feature Selection Algorithms. J. Mach. Learn. Res. 18 (2017)
James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Luján
Boosting Java Performance Using GPGPUs. ARCS (2017)
Sarah Nogueira, Konstantinos Sechidis, Gavin Brown
On the Use of Spearman's Rho to Measure the Stability of Feature Rankings. IbPRIA (2017)
Henry W. J. Reeve, Gavin Brown
Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours. ALT (2017)
Nikolaos Nikolaou, Efstratios Batzelis, Gavin Brown
Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions. DARE@PKDD/ECML (2017)
Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, Fabio Roli
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid. CoRR (2017)
Verónica Bolón-Canedo, Konstantinos Sechidis, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, Gavin Brown
Exploring the consequences of distributed feature selection in DNA microarray data. IJCNN (2017)
Konstantinos Sechidis, Emily Turner, Paul D. Metcalfe, James Weatherall, Gavin Brown
Ranking Biomarkers Through Mutual Information. CoRR (2016)
Sarah Nogueira, Gavin Brown
Measuring the Stability of Feature Selection. ECML/PKDD (2) (2016)
Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown
Cost-sensitive boosting algorithms: Do we really need them? Mach. Learn. 104 (2-3) (2016)
Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Gavin Brown
Estimating Mutual Information in Under-Reported Variables. Probabilistic Graphical Models (2016)
Paraskevas Yiapanis, Gavin Brown, Mikel Luján
Compiler-Driven Software Speculation for Thread-Level Parallelism. ACM Trans. Program. Lang. Syst. 38 (2) (2016)
Amir Ahmad, Gavin Brown
Random Ordinality Ensembles: Ensemble methods for multi-valued categorical data. Inf. Sci. 296 (2015)
Viachaslau Sazonau, Uli Sattler, Gavin Brown
General Terminology Induction in OWL. OWLED (2015)
Nikolaos Nikolaou, Gavin Brown
Calibrating AdaBoost for Asymmetric Learning. MCS (2015)
Gavin Brown
On unifiers, diversifiers, and the nature of pattern recognition. Pattern Recognit. Lett. 64 (2015)
Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli
Is Feature Selection Secure against Training Data Poisoning? ICML (2015)
Henry W. J. Reeve, Gavin Brown
Modular Autoencoders for Ensemble Feature Extraction. FE@NIPS (2015)
Viachaslau Sazonau, Uli Sattler, Gavin Brown
General Terminology Induction in OWL. ISWC (1) (2015)
Konstantinos Sechidis, Gavin Brown
Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data. ECML/PKDD (1) (2015)
Anthony Kleerekoper, Michael Pappas, Adam Craig Pocock, Gavin Brown, Mikel Luján
A scalable implementation of information theoretic feature selection for high dimensional data. IEEE BigData (2015)
Henry W. J. Reeve, Gavin Brown
Modular Autoencoders for Ensemble Feature Extraction. CoRR (2015)
James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Luján
Boosting Java Performance using GPGPUs. CoRR (2015)
Sarah Nogueira, Gavin Brown
Measuring the Stability of Feature Selection with Applications to Ensemble Methods. MCS (2015)
Amir Ahmad, Gavin Brown
Random Projection Random Discretization Ensembles - Ensembles of Linear Multivariate Decision Trees. IEEE Trans. Knowl. Data Eng. 26 (5) (2014)
Viachaslau Sazonau, Uli Sattler, Gavin Brown
Predicting Performance of OWL Reasoners: Locally or Globally? KR (2014)
Konstantinos Sechidis, Nikolaos Nikolaou, Gavin Brown
Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood. S+SSPR (2014)
Viachaslau Sazonau, Uli Sattler, Gavin Brown
Predicting OWL Reasoners: Locally or Globally? Description Logics (2014)
Konstantinos Sechidis, Borja Calvo, Gavin Brown
Statistical Hypothesis Testing in Positive Unlabelled Data. ECML/PKDD (3) (2014)
Ming-Jie Zhao, Narayanan Unny Edakunni, Adam Craig Pocock, Gavin Brown
Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications. J. Mach. Learn. Res. 14 (1) (2013)
Michele Filannino, Gavin Brown, Goran Nenadic
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge. SemEval@NAACL-HLT (2013)
Michele Filannino, Gavin Brown, Goran Nenadic
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge CoRR (2013)
Paraskevas Yiapanis, Demian Rosas-Ham, Gavin Brown, Mikel Luján
Optimizing software runtime systems for speculative parallelization. ACM Trans. Archit. Code Optim. 9 (4) (2013)
Anthony Kleerekoper, Mikel Luján, Gavin Brown
Exploring sketches for probability estimation with sublinear memory. IEEE BigData (2013)
Adam Craig Pocock, Mikel Luján, Gavin Brown
Informative Priors for Markov Blanket Discovery. AISTATS (2012)
Gavin Brown, Adam Craig Pocock, Ming-Jie Zhao, Mikel Luján
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection. J. Mach. Learn. Res. 13 (2012)
Tim Kovacs, Narayanan Unny Edakunni, Gavin Brown
Accuracy exponentiation in UCS and its effect on voting margins. GECCO (2011)
Richard John Stapenhurst, Gavin Brown
Theoretical and empirical analysis of diversity in non-stationary learning. CIDUE (2011)
Jeremy Singer, George Kovoor, Gavin Brown, Mikel Luján
Garbage collection auto-tuning for Java mapreduce on multi-cores. ISMM (2011)
Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs
Online, GA based mixture of experts: a probabilistic model of ucs. GECCO (2011)
Adam Craig Pocock, Paraskevas Yiapanis, Jeremy Singer, Mikel Luján, Gavin Brown
Online Non-stationary Boosting. MCS (2010)
Gavin Brown, Ludmila I. Kuncheva
"Good" and "Bad" Diversity in Majority Vote Ensembles. MCS (2010)
Tom Seaton, Gavin Brown, Julian F. Miller
Analytic Solutions to Differential Equations under Graph-Based Genetic Programming. EuroGP (2010)
Nikolas Ioannou, Jeremy Singer, Salman Khan, Polychronis Xekalakis, Paraskevas Yiapanis, Adam Craig Pocock, Gavin Brown, Mikel Luján, Ian Watson, Marcelo Cintra
Toward a more accurate understanding of the limits of the TLS execution paradigm. IISWC (2010)
Jeremy Singer, Gavin Brown, Mikel Luján, Adam Craig Pocock, Paraskevas Yiapanis
Fundamental Nano-Patterns to Characterize and Classify Java Methods. Electron. Notes Theor. Comput. Sci. 253 (7) (2010)
Jeremy Singer, Richard E. Jones, Gavin Brown, Mikel Luján
The economics of garbage collection. ISMM (2010)
Robi Polikar, Joseph DePasquale, Hussein Syed Mohammed, Gavin Brown, Ludmila I. Kuncheva
.MF: A random subspace approach for the missing feature problem. Pattern Recognit. 43 (11) (2010)
Gavin Brown
Some Thoughts at the Interface of Ensemble Methods and Feature Selection. MCS (2010)
Gavin Brown
A New Perspective for Information Theoretic Feature Selection. AISTATS (2009)
Narayanan Unny Edakunni, Tim Kovacs, Gavin Brown, James A. R. Marshall
Modeling UCS as a mixture of experts. GECCO (2009)
Gavin Brown
An Information Theoretic Perspective on Multiple Classifier Systems. MCS (2009)
Manuela Zanda, Gavin Brown
A Study of Semi-supervised Generative Ensembles. MCS (2009)
Amir Ahmad, Gavin Brown
A Study of Random Linear Oracle Ensembles. MCS (2009)
Amir Ahmad, Gavin Brown
Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical Data. MCS (2009)
Gavin Brown, Tim Kovacs, James A. R. Marshall
UCSpv: principled voting in UCS rule populations. GECCO (2007)
Manuela Zanda, Gavin Brown, Giorgio Fumera, Fabio Roli
Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation. MCS (2007)
James A. R. Marshall, Gavin Brown, Tim Kovacs
Bayesian estimation of rule accuracy in UCS. GECCO (Companion) (2007)
Stephen B. Furber, Gavin Brown, Joy Bose, J. Mike Cumpstey, P. Marshall, Jonathan L. Shapiro
Sparse Distributed Memory Using Rank-Order Neural Codes. IEEE Trans. Neural Networks 18 (3) (2007)
Jeremy Singer, Gavin Brown, Mikel Luján, Ian Watson
Towards intelligent analysis techniques for object pretenuring. PPPJ (2007)
Jeremy Singer, Gavin Brown, Ian Watson, John Cavazos
Intelligent selection of application-specific garbage collectors. ISMM (2007)
Jeremy Singer, Gavin Brown
Return Value Prediction meets Information Theory. Electron. Notes Theor. Comput. Sci. 164 (3) (2006)
Gavin Brown, Jeremy L. Wyatt, Ping Sun
Between Two Extremes: Examining Decompositions of the Ensemble Objective Function. Multiple Classifier Systems (2005)
Gavin Brown, Jeremy L. Wyatt, Rachel Harris, Xin Yao
Diversity creation methods: a survey and categorisation. Inf. Fusion 6 (1) (2005)
Gavin Brown, Jeremy L. Wyatt, Peter Tiño
Managing Diversity in Regression Ensembles. J. Mach. Learn. Res. 6 (2005)
Gavin Brown, Jeremy L. Wyatt
Negative Correlation Learning and the Ambiguity Family of Ensemble Methods. Multiple Classifier Systems (2003)
Gavin Brown, Jeremy L. Wyatt
The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods. ICML (2003)

Gavin Brown.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Gavin Brown