Gavin Brown

According to our database, Gavin Brown authored at least 89 papers between 2003 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

A max-affine spline approximation of neural networks using the Legendre transform of a convex-concave representation.

Adam Perrett, Danny Wood, Gavin Brown

CoRR, 2023

Tiny Classifier Circuits: Evolving Accelerators for Tabular Data.

Konstantinos Iordanou, Timothy Atkinson, Emre Ozer, Jedrzej Kufel, John Biggs, Gavin Brown, Mikel Luján

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

CoRR, 2023

2022

Bias-Variance Decompositions for Margin Losses.

Danny Wood, Tingting Mu, Gavin Brown

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

EnnCore: End-to-End Conceptual Guarding of Neural Architectures.

Edoardo Manino, Danilo S. Carvalho, Yi Dong, Julia Rozanova, Xidan Song, Mustafa A. Mustafa, André Freitas, Gavin Brown, Mikel Luján, Xiaowei Huang, Lucas C. Cordeiro

Proceedings of the Workshop on Artificial Intelligence Safety 2022 (SafeAI 2022) co-located with the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI2022), 2022

2020

Correction to: Efficient feature selection using shrinkage estimators.

Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown

Mach. Learn., 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

Knowl. Based Syst., 2020

Margin Maximization as Lossless Maximal Compression.

Nikolaos Nikolaou, Henry W. J. Reeve, Gavin Brown

CoRR, 2020

Better Boosting with Bandits for Online Learning.

Nikolaos Nikolaou, Joseph C. Mellor, Nikunj C. Oza, Gavin Brown

CoRR, 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

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2019

Efficient feature selection using shrinkage estimators.

Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown

Mach. Learn., 2019

Insights into distributed feature ranking.

Verónica Bolón-Canedo, Konstantinos Sechidis, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, Gavin Brown

Inf. Sci., 2019

Hybrid extreme learning machine approach for heterogeneous neural networks.

Vasileios Christou, Markos G. Tsipouras, Nikolaos Giannakeas, Alexandros T. Tzallas, Gavin Brown

Neurocomputing, 2019

Joint Training of Neural Network Ensembles.

Andrew M. Webb, Charles Reynolds, Dan-Andrei Iliescu, Henry W. J. Reeve, Mikel Luján, Gavin Brown

CoRR, 2019

ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM.

Andrew M. Webb, Gavin Brown, Mikel Luján

Proceedings of the Towards Autonomous Robotic Systems - 20th Annual Conference, 2019

On the Stability of Feature Selection in the Presence of Feature Correlations.

Konstantinos Sechidis, Konstantinos Papangelou, Sarah Nogueira, James Weatherall, Gavin Brown

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2018

Simple strategies for semi-supervised feature selection.

Konstantinos Sechidis, Gavin Brown

Mach. Learn., 2018

Diversity and degrees of freedom in regression ensembles.

Henry W. J. Reeve, Gavin Brown

Neurocomputing, 2018

Distinguishing prognostic and predictive biomarkers: an information theoretic approach.

Konstantinos Sechidis, Konstantinos Papangelou, Paul D. Metcalfe, David Svensson, James Weatherall, Gavin Brown

Bioinform., 2018

Modular Dimensionality Reduction.

Henry W. J. Reeve, Tingting Mu, Gavin Brown

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Toward an Understanding of Adversarial Examples in Clinical Trials.

Konstantinos Papangelou, Konstantinos Sechidis, James Weatherall, Gavin Brown

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

The K-Nearest Neighbour UCB Algorithm for Multi-Armed Bandits with Covariates.

Henry W. J. Reeve, Joe Mellor, Gavin Brown

Proceedings of the Algorithmic Learning Theory, 2018

2017

On the Stability of Feature Selection Algorithms.

Sarah Nogueira, Konstantinos Sechidis, Gavin Brown

J. Mach. Learn. Res., 2017

Dealing with under-reported variables: An information theoretic solution.

Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Mikel Luján, Gavin Brown

Int. J. Approx. Reason., 2017

Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions.

Nikolaos Nikolaou, Efstratios Batzelis, Gavin Brown

Proceedings of the Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy, 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

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid.

Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, Fabio Roli

Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

On the Use of Spearman's Rho to Measure the Stability of Feature Rankings.

Sarah Nogueira, Konstantinos Sechidis, Gavin Brown

Proceedings of the Pattern Recognition and Image Analysis - 8th Iberian Conference, 2017

Degrees of Freedom in Regression Ensembles.

Henry W. J. Reeve, Gavin Brown

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Mutual information for improving the efficiency of the SCH algorithm.

Diego Fernández-Francos, Oscar Fontenla-Romero, Amparo Alonso-Betanzos, Gavin Brown

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Boosting Java Performance Using GPGPUs.

James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Luján

Proceedings of the Architecture of Computing Systems - ARCS 2017, 2017

Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours.

Henry W. J. Reeve, Gavin Brown

Proceedings of the International Conference on Algorithmic Learning Theory, 2017

2016

Compiler-Driven Software Speculation for Thread-Level Parallelism.

Paraskevas Yiapanis, Gavin Brown, Mikel Luján

ACM Trans. Program. Lang. Syst., 2016

Cost-sensitive boosting algorithms: Do we really need them?

Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown

Mach. Learn., 2016

Ranking Biomarkers Through Mutual Information.

Konstantinos Sechidis, Emily Turner, Paul D. Metcalfe, James Weatherall, Gavin Brown

CoRR, 2016

Measuring the Stability of Feature Selection.

Sarah Nogueira, Gavin Brown

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Estimating Mutual Information in Under-Reported Variables.

Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Gavin Brown

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

2015

On unifiers, diversifiers, and the nature of pattern recognition.

Gavin Brown

Pattern Recognit. Lett., 2015

Random Ordinality Ensembles: Ensemble methods for multi-valued categorical data.

Amir Ahmad, Gavin Brown

Inf. Sci., 2015

General Terminology Induction in OWL.

Viachaslau Sazonau, Uli Sattler, Gavin Brown

Proceedings of the Semantic Web - ISWC 2015, 2015

Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data.

Konstantinos Sechidis, Gavin Brown

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Modular Autoencoders for Ensemble Feature Extraction.

Henry W. J. Reeve, Gavin Brown

Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

Measuring the Stability of Feature Selection with Applications to Ensemble Methods.

Sarah Nogueira, Gavin Brown

Proceedings of the Multiple Classifier Systems - 12th International Workshop, 2015

Calibrating AdaBoost for Asymmetric Learning.

Nikolaos Nikolaou, Gavin Brown

Proceedings of the Multiple Classifier Systems - 12th International Workshop, 2015

Is Feature Selection Secure against Training Data Poisoning?

Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli

Proceedings of the 32nd International Conference on Machine Learning, 2015

A scalable implementation of information theoretic feature selection for high dimensional data.

Anthony Kleerekoper, Michael Pappas, Adam Craig Pocock, Gavin Brown, Mikel Luján

Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015

2014

Random Projection Random Discretization Ensembles - Ensembles of Linear Multivariate Decision Trees.

Amir Ahmad, Gavin Brown

IEEE Trans. Knowl. Data Eng., 2014

Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood.

Konstantinos Sechidis, Nikolaos Nikolaou, Gavin Brown

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2014

Statistical Hypothesis Testing in Positive Unlabelled Data.

Konstantinos Sechidis, Borja Calvo, Gavin Brown

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Predicting Performance of OWL Reasoners: Locally or Globally?

Viachaslau Sazonau, Uli Sattler, Gavin Brown

Proceedings of the Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, 2014

Predicting OWL Reasoners: Locally or Globally?

Viachaslau Sazonau, Uli Sattler, Gavin Brown

Proceedings of the Informal Proceedings of the 27th International Workshop on Description Logics, 2014

2013

Optimizing software runtime systems for speculative parallelization.

Paraskevas Yiapanis, Demian Rosas-Ham, Gavin Brown, Mikel Luján

ACM Trans. Archit. Code Optim., 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

J. Mach. Learn. Res., 2013

ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge.

Michele Filannino, Gavin Brown, Goran Nenadic

Proceedings of the 7th International Workshop on Semantic Evaluation, 2013

Exploring sketches for probability estimation with sublinear memory.

Anthony Kleerekoper, Mikel Luján, Gavin Brown

Proceedings of the 2013 IEEE International Conference on Big Data (IEEE BigData 2013), 2013

2012

Informative Priors for Markov Blanket Discovery.

Adam Craig Pocock, Mikel Luján, Gavin Brown

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection.

Gavin Brown, Adam Craig Pocock, Ming-Jie Zhao, Mikel Luján

J. Mach. Learn. Res., 2012

2011

Garbage collection auto-tuning for Java mapreduce on multi-cores.

Jeremy Singer, George Kovoor, Gavin Brown, Mikel Luján

Proceedings of the 10th International Symposium on Memory Management, 2011

Accuracy exponentiation in UCS and its effect on voting margins.

Tim Kovacs, Narayanan Unny Edakunni, Gavin Brown

Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Online, GA based mixture of experts: a probabilistic model of ucs.

Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs

Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Theoretical and empirical analysis of diversity in non-stationary learning.

Richard John Stapenhurst, Gavin Brown

Proceedings of the 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, 2011

2010

Ensemble Learning.

Gavin Brown

Proceedings of the Encyclopedia of Machine Learning, 2010

Learn⁺⁺.MF: A random subspace approach for the missing feature problem.

Robi Polikar, Joseph DePasquale, Hussein Syed Mohammed, Gavin Brown, Ludmila I. Kuncheva

Pattern Recognit., 2010

Online Non-stationary Boosting.

Adam Craig Pocock, Paraskevas Yiapanis, Jeremy Singer, Mikel Luján, Gavin Brown

Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

"Good" and "Bad" Diversity in Majority Vote Ensembles.

Gavin Brown, Ludmila I. Kuncheva

Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

Some Thoughts at the Interface of Ensemble Methods and Feature Selection.

Gavin Brown

Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

The economics of garbage collection.

Jeremy Singer, Richard E. Jones, Gavin Brown, Mikel Luján

Proceedings of the 9th International Symposium on Memory Management, 2010

Toward a more accurate understanding of the limits of the TLS execution paradigm.

Nikolas Ioannou, Jeremy Singer, Salman Khan, Polychronis Xekalakis, Paraskevas Yiapanis, Adam Craig Pocock, Gavin Brown, Mikel Luján, Ian Watson, Marcelo Cintra

Proceedings of the 2010 IEEE International Symposium on Workload Characterization, 2010

Analytic Solutions to Differential Equations under Graph-Based Genetic Programming.

Tom Seaton, Gavin Brown, Julian F. Miller

Proceedings of the Genetic Programming, 13th European Conference, 2010

2009

A New Perspective for Information Theoretic Feature Selection.

Gavin Brown

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Fundamental Nano-Patterns to Characterize and Classify Java Methods.

Jeremy Singer, Gavin Brown, Mikel Luján, Adam Craig Pocock, Paraskevas Yiapanis

Proceedings of the Ninth Workshop on Language Descriptions Tools and Applications, 2009

A Study of Semi-supervised Generative Ensembles.

Manuela Zanda, Gavin Brown

Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

An Information Theoretic Perspective on Multiple Classifier Systems.

Gavin Brown

Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

A Study of Random Linear Oracle Ensembles.

Amir Ahmad, Gavin Brown

Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical Data.

Amir Ahmad, Gavin Brown

Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

Modeling UCS as a mixture of experts.

Narayanan Unny Edakunni, Tim Kovacs, Gavin Brown, James A. R. Marshall

Proceedings of the Genetic and Evolutionary Computation Conference, 2009

2007

Sparse Distributed Memory Using Rank-Order Neural Codes.

Stephen B. Furber, Gavin Brown, Joy Bose, J. Mike Cumpstey, P. Marshall, Jonathan L. Shapiro

IEEE Trans. Neural Networks, 2007

Towards intelligent analysis techniques for object pretenuring.

Jeremy Singer, Gavin Brown, Mikel Luján, Ian Watson

Proceedings of the 5th International Symposium on Principles and Practice of Programming in Java, 2007

Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation.

Manuela Zanda, Gavin Brown, Giorgio Fumera, Fabio Roli

Proceedings of the Multiple Classifier Systems, 7th International Workshop, 2007

Intelligent selection of application-specific garbage collectors.

Jeremy Singer, Gavin Brown, Ian Watson, John Cavazos

Proceedings of the 6th International Symposium on Memory Management, 2007

Bayesian estimation of rule accuracy in UCS.

James A. R. Marshall, Gavin Brown, Tim Kovacs

Proceedings of the Genetic and Evolutionary Computation Conference, 2007

UCSpv: principled voting in UCS rule populations.

Gavin Brown, Tim Kovacs, James A. R. Marshall

Proceedings of the Genetic and Evolutionary Computation Conference, 2007

2006

Return Value Prediction meets Information Theory.

Jeremy Singer, Gavin Brown

Proceedings of the 4th International Workshop on Quantitative Aspects of Programming Languages, 2006

2005

Managing Diversity in Regression Ensembles.

Gavin Brown, Jeremy L. Wyatt, Peter Tiño

J. Mach. Learn. Res., 2005

Diversity creation methods: a survey and categorisation.

Gavin Brown, Jeremy L. Wyatt, Rachel Harris, Xin Yao

Inf. Fusion, 2005

Between Two Extremes: Examining Decompositions of the Ensemble Objective Function.

Gavin Brown, Jeremy L. Wyatt, Ping Sun

Proceedings of the Multiple Classifier Systems, 6th International Workshop, 2005

2004

Diversity in neural network ensembles.

Gavin Brown

PhD thesis, 2004

2003

Negative Correlation Learning and the Ambiguity Family of Ensemble Methods.

Gavin Brown, Jeremy L. Wyatt

Proceedings of the Multiple Classifier Systems, 4th International Workshop, 2003

The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods.

Gavin Brown, Jeremy L. Wyatt

Proceedings of the Machine Learning, 2003