Gavin Brown - RankMe

106 papers A* 3A 7B 6C 3Misc 2Journal 42Unranked 40

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Bias/Variance is not the same as Approximation/Estimation. Trans. Mach. Learn. Res. Gavin Brown, Riccardo Ali
2024	Misc	`conf`	FINESSD: Near-Storage Feature Selection with Mutual Information for Resource-Limited FPGAs. FCCM Nikolaos Kyparissas, Gavin Brown, Mikel Luján
2023	J	`jnl`	A Unified Theory of Diversity in Ensemble Learning. CoRR Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Luján, Gavin Brown
2023	J	`jnl`	A Unified Theory of Diversity in Ensemble Learning. J. Mach. Learn. Res. Danny Wood, Tingting Mu, Andrew M. Webb, Henry W. J. Reeve, Mikel Luján, Gavin Brown
2023	J	`jnl`	A max-affine spline approximation of neural networks using the Legendre transform of a convex-concave representation. CoRR Adam Perrett, Danny Wood, Gavin Brown
2023	—	`conf`	Outlier detection of vital sign trajectories from COVID-19 patients. EMBC Sara Summerton, Ann Tivey, Rohan Shotton, Gavin Brown, Oliver C. Redfern, Rachel Oakley, John Radford, David C. Wong
2023	J	`jnl`	Tiny Classifier Circuits: Evolving Accelerators for Tabular Data. CoRR Konstantinos Iordanou, Timothy Atkinson, Emre Ozer, Jedrzej Kufel, John Biggs, Gavin Brown, Mikel Luján
2022	A	`conf`	Bias-Variance Decompositions for Margin Losses. AISTATS Danny Wood, Tingting Mu, Gavin Brown
2022	J	`jnl`	Bias-Variance Decompositions for Margin Losses. CoRR Danny Wood, Tingting Mu, Gavin Brown
2022	—	`conf`	EnnCore: End-to-End Conceptual Guarding of Neural Architectures. SafeAI@AAAI 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
2022	J	`jnl`	Outlier detection of vital sign trajectories from COVID-19 patients. CoRR Sara Summerton, Ann Tivey, Rohan Shotton, Gavin Brown, Oliver C. Redfern, Rachel Oakley, John Radford, David C. Wong
2020	J	`jnl`	Better Boosting with Bandits for Online Learning. CoRR Nikolaos Nikolaou, Joseph C. Mellor, Nikunj C. Oza, Gavin Brown
2020	J	`jnl`	Correction to: Efficient feature selection using shrinkage estimators. Mach. Learn. Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown
2020	J	`jnl`	Feature selection with limited bit depth mutual information for portable embedded systems. Knowl. Based Syst. Laura Morán-Fernández, Konstantinos Sechidis, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, Gavin Brown
2020	J	`jnl`	Margin Maximization as Lossless Maximal Compression. CoRR Nikolaos Nikolaou, Henry W. J. Reeve, Gavin Brown
2020	—	`conf`	To Ensemble or Not Ensemble: When Does End-to-End Training Fail? ECML/PKDD (3) Andrew M. Webb, Charles Reynolds, Wenlin Chen, Henry W. J. Reeve, Dan-Andrei Iliescu, Mikel Luján, Gavin Brown
2019	J	`jnl`	Efficient feature selection using shrinkage estimators. Mach. Learn. Konstantinos Sechidis, Laura Azzimonti, Adam Craig Pocock, Giorgio Corani, James Weatherall, Gavin Brown
2019	J	`jnl`	Hybrid extreme learning machine approach for heterogeneous neural networks. Neurocomputing Vasileios Christou, Markos G. Tsipouras, Nikolaos Giannakeas, Alexandros T. Tzallas, Gavin Brown
2019	J	`jnl`	Insights into distributed feature ranking. Inf. Sci. Verónica Bolón-Canedo, Konstantinos Sechidis, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, Gavin Brown
2019	J	`jnl`	Joint Training of Neural Network Ensembles. CoRR Andrew M. Webb, Charles Reynolds, Dan-Andrei Iliescu, Henry W. J. Reeve, Mikel Luján, Gavin Brown
2019	—	`conf`	ORB-SLAM-CNN: Lessons in Adding Semantic Map Construction to Feature-Based SLAM. TAROS (1) Andrew M. Webb, Gavin Brown, Mikel Luján
2019	—	`conf`	On the Stability of Feature Selection in the Presence of Feature Correlations. ECML/PKDD (1) Konstantinos Sechidis, Konstantinos Papangelou, Sarah Nogueira, James Weatherall, Gavin Brown
2018	J	`jnl`	Distinguishing prognostic and predictive biomarkers: an information theoretic approach. Bioinform. Konstantinos Sechidis, Konstantinos Papangelou, Paul D. Metcalfe, David Svensson, James Weatherall, Gavin Brown
2018	J	`jnl`	Distinguishing prognostic and predictive biomarkers: an information theoretic approach. Bioinform. Konstantinos Sechidis, Konstantinos Papangelou, Paul D. Metcalfe, David Svensson, James Weatherall, Gavin Brown
2018	J	`jnl`	Diversity and degrees of freedom in regression ensembles. CoRR Henry W. J. Reeve, Gavin Brown
2018	J	`jnl`	Diversity and degrees of freedom in regression ensembles. Neurocomputing Henry W. J. Reeve, Gavin Brown
2018	J	`jnl`	Is feature selection secure against training data poisoning? CoRR Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli
2018	J	`jnl`	Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours. CoRR Henry W. J. Reeve, Gavin Brown
2018	—	`conf`	Modular Dimensionality Reduction. ECML/PKDD (1) Henry W. J. Reeve, Tingting Mu, Gavin Brown
2018	J	`jnl`	Simple strategies for semi-supervised feature selection. Mach. Learn. Konstantinos Sechidis, Gavin Brown
2018	B	`conf`	The K-Nearest Neighbour UCB Algorithm for Multi-Armed Bandits with Covariates. ALT Henry W. J. Reeve, Joe Mellor, Gavin Brown
2018	J	`jnl`	The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates. CoRR Henry W. J. Reeve, Joe Mellor, Gavin Brown
2018	—	`conf`	Toward an Understanding of Adversarial Examples in Clinical Trials. ECML/PKDD (1) Konstantinos Papangelou, Konstantinos Sechidis, James Weatherall, Gavin Brown
2017	—	`conf`	Boosting Java Performance Using GPGPUs. ARCS James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Luján
2017	J	`jnl`	Dealing with under-reported variables: An information theoretic solution. Int. J. Approx. Reason. Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Mikel Luján, Gavin Brown
2017	B	`conf`	Degrees of Freedom in Regression Ensembles. ESANN Henry W. J. Reeve, Gavin Brown
2017	B	`conf`	Exploring the consequences of distributed feature selection in DNA microarray data. IJCNN Verónica Bolón-Canedo, Konstantinos Sechidis, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, Gavin Brown
2017	—	`conf`	Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions. DARE@PKDD/ECML Nikolaos Nikolaou, Efstratios Batzelis, Gavin Brown
2017	—	`conf`	Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid. ICCV Workshops Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, Fabio Roli
2017	J	`jnl`	Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid. CoRR Marco Melis, Ambra Demontis, Battista Biggio, Gavin Brown, Giorgio Fumera, Fabio Roli
2017	B	`conf`	Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours. ALT Henry W. J. Reeve, Gavin Brown
2017	B	`conf`	Mutual information for improving the efficiency of the SCH algorithm. ESANN Diego Fernández-Francos, Oscar Fontenla-Romero, Amparo Alonso-Betanzos, Gavin Brown
2017	J	`jnl`	On the Stability of Feature Selection Algorithms. J. Mach. Learn. Res. Sarah Nogueira, Konstantinos Sechidis, Gavin Brown
2017	Misc	`conf`	On the Use of Spearman's Rho to Measure the Stability of Feature Rankings. IbPRIA Sarah Nogueira, Konstantinos Sechidis, Gavin Brown
2016	J	`jnl`	Compiler-Driven Software Speculation for Thread-Level Parallelism. ACM Trans. Program. Lang. Syst. Paraskevas Yiapanis, Gavin Brown, Mikel Luján
2016	J	`jnl`	Cost-sensitive boosting algorithms: Do we really need them? Mach. Learn. Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown
2016	—	`conf`	Estimating Mutual Information in Under-Reported Variables. Probabilistic Graphical Models Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Gavin Brown
2016	—	`conf`	Measuring the Stability of Feature Selection. ECML/PKDD (2) Sarah Nogueira, Gavin Brown
2016	J	`jnl`	Ranking Biomarkers Through Mutual Information. CoRR Konstantinos Sechidis, Emily Turner, Paul D. Metcalfe, James Weatherall, Gavin Brown
2015	—	`conf`	A scalable implementation of information theoretic feature selection for high dimensional data. IEEE BigData Anthony Kleerekoper, Michael Pappas, Adam Craig Pocock, Gavin Brown, Mikel Luján
2015	J	`jnl`	Boosting Java Performance using GPGPUs. CoRR James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Luján
2015	—	`conf`	Calibrating AdaBoost for Asymmetric Learning. MCS Nikolaos Nikolaou, Gavin Brown
2015	—	`conf`	General Terminology Induction in OWL. OWLED Viachaslau Sazonau, Uli Sattler, Gavin Brown
2015	—	`conf`	General Terminology Induction in OWL. ISWC (1) Viachaslau Sazonau, Uli Sattler, Gavin Brown
2015	A*	`conf`	Is Feature Selection Secure against Training Data Poisoning? ICML Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli
2015	—	`conf`	Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data. ECML/PKDD (1) Konstantinos Sechidis, Gavin Brown
2015	—	`conf`	Measuring the Stability of Feature Selection with Applications to Ensemble Methods. MCS Sarah Nogueira, Gavin Brown
2015	—	`conf`	Modular Autoencoders for Ensemble Feature Extraction. FE@NIPS Henry W. J. Reeve, Gavin Brown
2015	J	`jnl`	Modular Autoencoders for Ensemble Feature Extraction. CoRR Henry W. J. Reeve, Gavin Brown
2015	J	`jnl`	On unifiers, diversifiers, and the nature of pattern recognition. Pattern Recognit. Lett. Gavin Brown
2015	J	`jnl`	Random Ordinality Ensembles: Ensemble methods for multi-valued categorical data. Inf. Sci. Amir Ahmad, Gavin Brown
2014	—	`conf`	Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood. S+SSPR Konstantinos Sechidis, Nikolaos Nikolaou, Gavin Brown
2014	—	`conf`	Predicting OWL Reasoners: Locally or Globally? Description Logics Viachaslau Sazonau, Uli Sattler, Gavin Brown
2014	A*	`conf`	Predicting Performance of OWL Reasoners: Locally or Globally? KR Viachaslau Sazonau, Uli Sattler, Gavin Brown
2014	J	`jnl`	Random Projection Random Discretization Ensembles - Ensembles of Linear Multivariate Decision Trees. IEEE Trans. Knowl. Data Eng. Amir Ahmad, Gavin Brown
2014	—	`conf`	Statistical Hypothesis Testing in Positive Unlabelled Data. ECML/PKDD (3) Konstantinos Sechidis, Borja Calvo, Gavin Brown
2014	—	`ed.`	Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings S+SSPR Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, Marcello Pelillo
2013	J	`jnl`	Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications. J. Mach. Learn. Res. Ming-Jie Zhao, Narayanan Unny Edakunni, Adam Craig Pocock, Gavin Brown
2013	—	`conf`	Exploring sketches for probability estimation with sublinear memory. IEEE BigData Anthony Kleerekoper, Mikel Luján, Gavin Brown
2013	J	`jnl`	ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge CoRR Michele Filannino, Gavin Brown, Goran Nenadic
2013	—	`conf`	ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge. SemEval@NAACL-HLT Michele Filannino, Gavin Brown, Goran Nenadic
2013	J	`jnl`	Optimizing software runtime systems for speculative parallelization. ACM Trans. Archit. Code Optim. Paraskevas Yiapanis, Demian Rosas-Ham, Gavin Brown, Mikel Luján
2012	J	`jnl`	Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection. J. Mach. Learn. Res. Gavin Brown, Adam Craig Pocock, Ming-Jie Zhao, Mikel Luján
2012	A	`conf`	Informative Priors for Markov Blanket Discovery. AISTATS Adam Craig Pocock, Mikel Luján, Gavin Brown
2011	A	`conf`	Accuracy exponentiation in UCS and its effect on voting margins. GECCO Tim Kovacs, Narayanan Unny Edakunni, Gavin Brown
2011	C	`conf`	Garbage collection auto-tuning for Java mapreduce on multi-cores. ISMM Jeremy Singer, George Kovoor, Gavin Brown, Mikel Luján
2011	A	`conf`	Online, GA based mixture of experts: a probabilistic model of ucs. GECCO Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs
2011	—	`conf`	Theoretical and empirical analysis of diversity in non-stationary learning. CIDUE Richard John Stapenhurst, Gavin Brown
2010	—	`conf`	"Good" and "Bad" Diversity in Majority Vote Ensembles. MCS Gavin Brown, Ludmila I. Kuncheva
2010	B	`conf`	Analytic Solutions to Differential Equations under Graph-Based Genetic Programming. EuroGP Tom Seaton, Gavin Brown, Julian F. Miller
2010	—	`ch.`	Ensemble Learning. Encyclopedia of Machine Learning Gavin Brown
2010	J	`jnl`	Learn Pattern Recognit. Robi Polikar, Joseph DePasquale, Hussein Syed Mohammed, Gavin Brown, Ludmila I. Kuncheva
2010	—	`conf`	Online Non-stationary Boosting. MCS Adam Craig Pocock, Paraskevas Yiapanis, Jeremy Singer, Mikel Luján, Gavin Brown
2010	—	`conf`	Some Thoughts at the Interface of Ensemble Methods and Feature Selection. MCS Gavin Brown
2010	C	`conf`	The economics of garbage collection. ISMM Jeremy Singer, Richard E. Jones, Gavin Brown, Mikel Luján
2010	—	`conf`	Toward a more accurate understanding of the limits of the TLS execution paradigm. IISWC Nikolas Ioannou, Jeremy Singer, Salman Khan, Polychronis Xekalakis, Paraskevas Yiapanis, Adam Craig Pocock, Gavin Brown, Mikel Luján, Ian Watson, Marcelo Cintra
2009	A	`conf`	A New Perspective for Information Theoretic Feature Selection. AISTATS Gavin Brown
2009	—	`conf`	A Study of Random Linear Oracle Ensembles. MCS Amir Ahmad, Gavin Brown
2009	—	`conf`	A Study of Semi-supervised Generative Ensembles. MCS Manuela Zanda, Gavin Brown
2009	—	`conf`	An Information Theoretic Perspective on Multiple Classifier Systems. MCS Gavin Brown
2009	—	`conf`	Fundamental Nano-Patterns to Characterize and Classify Java Methods. LDTA Jeremy Singer, Gavin Brown, Mikel Luján, Adam Craig Pocock, Paraskevas Yiapanis
2009	A	`conf`	Modeling UCS as a mixture of experts. GECCO Narayanan Unny Edakunni, Tim Kovacs, Gavin Brown, James A. R. Marshall
2009	—	`conf`	Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical Data. MCS Amir Ahmad, Gavin Brown
2007	—	`conf`	Bayesian estimation of rule accuracy in UCS. GECCO (Companion) James A. R. Marshall, Gavin Brown, Tim Kovacs
2007	—	`conf`	Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation. MCS Manuela Zanda, Gavin Brown, Giorgio Fumera, Fabio Roli
2007	C	`conf`	Intelligent selection of application-specific garbage collectors. ISMM Jeremy Singer, Gavin Brown, Ian Watson, John Cavazos
2007	J	`jnl`	Sparse Distributed Memory Using Rank-Order Neural Codes. IEEE Trans. Neural Networks Stephen B. Furber, Gavin Brown, Joy Bose, J. Mike Cumpstey, P. Marshall, Jonathan L. Shapiro
2007	—	`conf`	Towards intelligent analysis techniques for object pretenuring. PPPJ Jeremy Singer, Gavin Brown, Mikel Luján, Ian Watson
2007	A	`conf`	UCSpv: principled voting in UCS rule populations. GECCO Gavin Brown, Tim Kovacs, James A. R. Marshall
2006	—	`conf`	Return Value Prediction meets Information Theory. QAPL Jeremy Singer, Gavin Brown
2005	—	`conf`	Between Two Extremes: Examining Decompositions of the Ensemble Objective Function. Multiple Classifier Systems Gavin Brown, Jeremy L. Wyatt, Ping Sun
2005	J	`jnl`	Diversity creation methods: a survey and categorisation. Inf. Fusion Gavin Brown, Jeremy L. Wyatt, Rachel Harris, Xin Yao
2005	J	`jnl`	Managing Diversity in Regression Ensembles. J. Mach. Learn. Res. Gavin Brown, Jeremy L. Wyatt, Peter Tiño
2004	—	`—`	Diversity in neural network ensembles. Gavin Brown
2003	—	`conf`	Negative Correlation Learning and the Ambiguity Family of Ensemble Methods. Multiple Classifier Systems Gavin Brown, Jeremy L. Wyatt
2003	A*	`conf`	The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods. ICML Gavin Brown, Jeremy L. Wyatt