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ORCIDGavin Brown 0001
Person information
- affiliation: University of Manchester, UK
Other persons with the same name
- Gavin Brown — disambiguation page
- Gavin Brown 0002 — University of Sydney, NSW, Australia
- Gavin Brown 0003 — Boston University, MA, USA
- Gavin Brown 0004 — University of Warwick, Coventry, UK
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2020 – today
- 2024
[j25]Gavin Brown, Riccardo Ali:
Bias/Variance is not the same as Approximation/Estimation. Trans. Mach. Learn. Res. 2024 (2024)- [c60]Nikolaos Kyparissas
, Gavin Brown, Mikel Luján:
FINESSD: Near-Storage Feature Selection with Mutual Information for Resource-Limited FPGAs. FCCM 2024: 173-184 - 2023
- [j24]Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Luján, Gavin Brown:
A Unified Theory of Diversity in Ensemble Learning. J. Mach. Learn. Res. 24: 359:1-359:49 (2023) - [i16]Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Luján, Gavin Brown:
A Unified Theory of Diversity in Ensemble Learning. CoRR abs/2301.03962 (2023) - [i15]Konstantinos Iordanou, Timothy Atkinson, Emre Ozer, Jedrzej Kufel, John Biggs, Gavin Brown, Mikel Luján:
Tiny Classifier Circuits: Evolving Accelerators for Tabular Data. CoRR abs/2303.00031 (2023) - [i14]Adam Perrett, Danny Wood, Gavin Brown:
A max-affine spline approximation of neural networks using the Legendre transform of a convex-concave representation. CoRR abs/2307.09602 (2023) - 2022
- [c59]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:
EnnCore: End-to-End Conceptual Guarding of Neural Architectures. SafeAI@AAAI 2022 - [c58]Danny Wood, Tingting Mu, Gavin Brown:
Bias-Variance Decompositions for Margin Losses. AISTATS 2022: 1975-2001 - [i13]Danny Wood, Tingting Mu, Gavin Brown:
Bias-Variance Decompositions for Margin Losses. CoRR abs/2204.12155 (2022) - 2020
- [j23]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: 105885 (2020) - [j22]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): 1565-1567 (2020) - [c57]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: 109-123 - [i12]Nikolaos Nikolaou, Joseph C. Mellor, Nikunj C. Oza, Gavin Brown:
Better Boosting with Bandits for Online Learning. CoRR abs/2001.06105 (2020) - [i11]Nikolaos Nikolaou, Henry W. J. Reeve, Gavin Brown:
Margin Maximization as Lossless Maximal Compression. CoRR abs/2001.10318 (2020)
2010 – 2019
- 2019
- [j21]Vasileios Christou, Markos G. Tsipouras, Nikolaos Giannakeas, Alexandros T. Tzallas, Gavin Brown:
Hybrid extreme learning machine approach for heterogeneous neural networks. Neurocomputing 361: 137-150 (2019) - [j20]Verónica Bolón-Canedo, Konstantinos Sechidis, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, Gavin Brown:
Insights into distributed feature ranking. Inf. Sci. 496: 378-398 (2019) - [j19]Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown:
Efficient feature selection using shrinkage estimators. Mach. Learn. 108(8-9): 1261-1286 (2019) - [c56]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: 327-342 - [c55]Andrew M. Webb, Gavin Brown, Mikel Luján:
ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM. TAROS (1) 2019: 221-235 - [i10]Andrew M. Webb, Charles Reynolds, Dan-Andrei Iliescu, Henry W. J. Reeve, Mikel Luján, Gavin Brown:
Joint Training of Neural Network Ensembles. CoRR abs/1902.04422 (2019) - 2018
- [j18]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): 3365-3376 (2018) - [j17]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): 4139 (2018) - [j16]Henry W. J. Reeve, Gavin Brown:
Diversity and degrees of freedom in regression ensembles. Neurocomputing 298: 55-68 (2018) - [j15]Konstantinos Sechidis, Gavin Brown:
Simple strategies for semi-supervised feature selection. Mach. Learn. 107(2): 357-395 (2018) - [c54]Henry W. J. Reeve, Joe Mellor, Gavin Brown:
The K-Nearest Neighbour UCB Algorithm for Multi-Armed Bandits with Covariates. ALT 2018: 725-752 - [c53]Konstantinos Papangelou, Konstantinos Sechidis, James Weatherall, Gavin Brown:
Toward an Understanding of Adversarial Examples in Clinical Trials. ECML/PKDD (1) 2018: 35-51 - [c52]Henry W. J. Reeve, Tingting Mu, Gavin Brown:
Modular Dimensionality Reduction. ECML/PKDD (1) 2018: 605-619 - [i9]Henry W. J. Reeve, Gavin Brown:
Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours. CoRR abs/1803.00310 (2018) - [i8]Henry W. J. Reeve, Gavin Brown:
Diversity and degrees of freedom in regression ensembles. CoRR abs/1803.00314 (2018) - [i7]Henry W. J. Reeve, Joe Mellor, Gavin Brown:
The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates. CoRR abs/1803.00316 (2018) - [i6]Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli:
Is feature selection secure against training data poisoning? CoRR abs/1804.07933 (2018) - 2017
- [j14]Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Mikel Luján, Gavin Brown:
Dealing with under-reported variables: An information theoretic solution. Int. J. Approx. Reason. 85: 159-177 (2017) - [j13]Sarah Nogueira, Konstantinos Sechidis, Gavin Brown:
On the Stability of Feature Selection Algorithms. J. Mach. Learn. Res. 18: 174:1-174:54 (2017) - [c51]Henry W. J. Reeve, Gavin Brown:
Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours. ALT 2017: 11-56 - [c50]James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Luján:
Boosting Java Performance Using GPGPUs. ARCS 2017: 59-70 - [c49]Diego Fernández-Francos, Oscar Fontenla-Romero, Amparo Alonso-Betanzos, Gavin Brown:
Mutual information for improving the efficiency of the SCH algorithm. ESANN 2017 - [c48]Henry W. J. Reeve, Gavin Brown:
Degrees of Freedom in Regression Ensembles. ESANN 2017 - [c47]Sarah Nogueira, Konstantinos Sechidis, Gavin Brown:
On the Use of Spearman's Rho to Measure the Stability of Feature Rankings. IbPRIA 2017: 381-391 - [c46]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: 751-759 - [c45]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: 1665-1672 - [c44]Nikolaos Nikolaou, Efstratios Batzelis, Gavin Brown:
Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions. DARE@PKDD/ECML 2017: 13-25 - [i5]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 abs/1708.06939 (2017) - 2016
- [j12]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): 359-384 (2016) - [j11]Paraskevas Yiapanis, Gavin Brown, Mikel Luján:
Compiler-Driven Software Speculation for Thread-Level Parallelism. ACM Trans. Program. Lang. Syst. 38(2): 5:1-5:45 (2016) - [c43]Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Gavin Brown:
Estimating Mutual Information in Under-Reported Variables. Probabilistic Graphical Models 2016: 449-461 - [c42]Sarah Nogueira, Gavin Brown:
Measuring the Stability of Feature Selection. ECML/PKDD (2) 2016: 442-457 - [i4]Konstantinos Sechidis, Emily Turner, Paul D. Metcalfe, James Weatherall, Gavin Brown:
Ranking Biomarkers Through Mutual Information. CoRR abs/1612.01316 (2016) - 2015
- [j10]Amir Ahmad, Gavin Brown:
Random Ordinality Ensembles: Ensemble methods for multi-valued categorical data. Inf. Sci. 296: 75-94 (2015) - [j9]Gavin Brown:
On unifiers, diversifiers, and the nature of pattern recognition. Pattern Recognit. Lett. 64: 11-20 (2015) - [c41]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: 339-346 - [c40]Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli:
Is Feature Selection Secure against Training Data Poisoning? ICML 2015: 1689-1698 - [c39]Nikolaos Nikolaou, Gavin Brown:
Calibrating AdaBoost for Asymmetric Learning. MCS 2015: 112-124 - [c38]Sarah Nogueira, Gavin Brown:
Measuring the Stability of Feature Selection with Applications to Ensemble Methods. MCS 2015: 135-146 - [c37]Henry W. J. Reeve, Gavin Brown:
Modular Autoencoders for Ensemble Feature Extraction. FE@NIPS 2015: 242-259 - [c36]Viachaslau Sazonau, Uli Sattler, Gavin Brown:
General Terminology Induction in OWL. OWLED 2015: 1-13 - [c35]Konstantinos Sechidis, Gavin Brown:
Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data. ECML/PKDD (1) 2015: 351-366 - [c34]Viachaslau Sazonau, Uli Sattler, Gavin Brown:
General Terminology Induction in OWL. ISWC (1) 2015: 533-550 - [i3]James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Luján:
Boosting Java Performance using GPGPUs. CoRR abs/1508.06791 (2015) - [i2]Henry W. J. Reeve, Gavin Brown:
Modular Autoencoders for Ensemble Feature Extraction. CoRR abs/1511.07340 (2015) - 2014
- [j8]Amir Ahmad, Gavin Brown:
Random Projection Random Discretization Ensembles - Ensembles of Linear Multivariate Decision Trees. IEEE Trans. Knowl. Data Eng. 26(5): 1225-1239 (2014) - [c33]Viachaslau Sazonau, Uli Sattler, Gavin Brown:
Predicting OWL Reasoners: Locally or Globally? Description Logics 2014: 713-724 - [c32]Viachaslau Sazonau, Uli Sattler, Gavin Brown:
Predicting Performance of OWL Reasoners: Locally or Globally? KR 2014 - [c31]Konstantinos Sechidis, Borja Calvo, Gavin Brown:
Statistical Hypothesis Testing in Positive Unlabelled Data. ECML/PKDD (3) 2014: 66-81 - [c30]Konstantinos Sechidis, Nikolaos Nikolaou, Gavin Brown:
Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood. S+SSPR 2014: 143-152 - [e1]Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, Marcello Pelillo:
Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings. Lecture Notes in Computer Science 8621, Springer 2014, ISBN 978-3-662-44414-6 [contents] - 2013
- [j7]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): 1033-1090 (2013) - [j6]Paraskevas Yiapanis, Demian Rosas-Ham, Gavin Brown, Mikel Luján:
Optimizing software runtime systems for speculative parallelization. ACM Trans. Archit. Code Optim. 9(4): 39:1-39:27 (2013) - [c29]Anthony Kleerekoper, Mikel Luján, Gavin Brown:
Exploring sketches for probability estimation with sublinear memory. IEEE BigData 2013: 79-86 - [c28]Michele Filannino, Gavin Brown, Goran Nenadic:
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge. SemEval@NAACL-HLT 2013: 53-57 - [i1]Michele Filannino, Gavin Brown, Goran Nenadic:
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge. CoRR abs/1304.7942 (2013) - 2012
- [j5]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: 27-66 (2012) - [c27]Adam Craig Pocock, Mikel Luján, Gavin Brown:
Informative Priors for Markov Blanket Discovery. AISTATS 2012: 905-913 - 2011
- [c26]Richard John Stapenhurst, Gavin Brown:
Theoretical and empirical analysis of diversity in non-stationary learning. CIDUE 2011: 25-32 - [c25]Tim Kovacs, Narayanan Unny Edakunni, Gavin Brown:
Accuracy exponentiation in UCS and its effect on voting margins. GECCO 2011: 1251-1258 - [c24]Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs:
Online, GA based mixture of experts: a probabilistic model of ucs. GECCO 2011: 1267-1274 - [c23]Jeremy Singer, George Kovoor, Gavin Brown, Mikel Luján:
Garbage collection auto-tuning for Java mapreduce on multi-cores. ISMM 2011: 109-118 - 2010
- [j4]Robi Polikar, Joseph DePasquale, Hussein Syed Mohammed, Gavin Brown, Ludmila I. Kuncheva:
Learn++.MF: A random subspace approach for the missing feature problem. Pattern Recognit. 43(11): 3817-3832 (2010) - [c22]Tom Seaton, Gavin Brown, Julian F. Miller:
Analytic Solutions to Differential Equations under Graph-Based Genetic Programming. EuroGP 2010: 232-243 - [c21]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: 1-12 - [c20]Jeremy Singer, Richard E. Jones, Gavin Brown, Mikel Luján:
The economics of garbage collection. ISMM 2010: 103-112 - [c19]Gavin Brown, Ludmila I. Kuncheva:
"Good" and "Bad" Diversity in Majority Vote Ensembles. MCS 2010: 124-133 - [c18]Adam Craig Pocock, Paraskevas Yiapanis, Jeremy Singer, Mikel Luján, Gavin Brown:
Online Non-stationary Boosting. MCS 2010: 205-214 - [c17]Gavin Brown:
Some Thoughts at the Interface of Ensemble Methods and Feature Selection. MCS 2010: 314 - [r1]Gavin Brown:
Ensemble Learning. Encyclopedia of Machine Learning 2010: 312-320
2000 – 2009
- 2009
- [c16]Narayanan Unny Edakunni, Tim Kovacs, Gavin Brown, James A. R. Marshall:
Modeling UCS as a mixture of experts. GECCO 2009: 1187-1194 - [c15]Amir Ahmad, Gavin Brown:
Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical Data. MCS 2009: 222-231 - [c14]Manuela Zanda, Gavin Brown:
A Study of Semi-supervised Generative Ensembles. MCS 2009: 242-251 - [c13]Gavin Brown:
An Information Theoretic Perspective on Multiple Classifier Systems. MCS 2009: 344-353 - [c12]Amir Ahmad, Gavin Brown:
A Study of Random Linear Oracle Ensembles. MCS 2009: 488-497 - [c11]Jeremy Singer, Gavin Brown, Mikel Luján, Adam Craig Pocock, Paraskevas Yiapanis:
Fundamental Nano-Patterns to Characterize and Classify Java Methods. LDTA 2009: 191-204 - [c10]Gavin Brown:
A New Perspective for Information Theoretic Feature Selection. AISTATS 2009: 49-56 - 2007
- [j3]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): 648-659 (2007) - [c9]Gavin Brown, Tim Kovacs, James A. R. Marshall:
UCSpv: principled voting in UCS rule populations. GECCO 2007: 1774-1781 - [c8]James A. R. Marshall, Gavin Brown, Tim Kovacs:
Bayesian estimation of rule accuracy in UCS. GECCO (Companion) 2007: 2831-2834 - [c7]Jeremy Singer, Gavin Brown, Ian Watson, John Cavazos:
Intelligent selection of application-specific garbage collectors. ISMM 2007: 91-102 - [c6]Manuela Zanda, Gavin Brown, Giorgio Fumera, Fabio Roli:
Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation. MCS 2007: 440-449 - [c5]Jeremy Singer, Gavin Brown, Mikel Luján, Ian Watson:
Towards intelligent analysis techniques for object pretenuring. PPPJ 2007: 203-208 - 2006
- [c4]Jeremy Singer, Gavin Brown:
Return Value Prediction meets Information Theory. QAPL 2006: 137-151 - 2005
- [j2]Gavin Brown, Jeremy L. Wyatt, Rachel Harris, Xin Yao:
Diversity creation methods: a survey and categorisation. Inf. Fusion 6(1): 5-20 (2005) - [j1]Gavin Brown, Jeremy L. Wyatt, Peter Tiño:
Managing Diversity in Regression Ensembles. J. Mach. Learn. Res. 6: 1621-1650 (2005) - [c3]Gavin Brown, Jeremy L. Wyatt, Ping Sun:
Between Two Extremes: Examining Decompositions of the Ensemble Objective Function. Multiple Classifier Systems 2005: 296-305 - 2004
- [b1]Gavin Brown:
Diversity in neural network ensembles. University of Birmingham, UK, 2004 - 2003
- [c2]Gavin Brown, Jeremy L. Wyatt:
The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods. ICML 2003: 67-74 - [c1]Gavin Brown, Jeremy L. Wyatt:
Negative Correlation Learning and the Ambiguity Family of Ensemble Methods. Multiple Classifier Systems 2003: 266-275
Coauthor Index
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