Gavin Brown

According to our database1, Gavin Brown
  • authored at least 73 papers between 2003 and 2018.
  • has a "Dijkstra number"2 of five.

Timeline

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Bibliography

2018
Simple strategies for semi-supervised feature selection.
Machine Learning, 2018

The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates.
CoRR, 2018

Diversity and degrees of freedom in regression ensembles.
CoRR, 2018

Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours.
CoRR, 2018

The K-Nearest Neighbour UCB Algorithm for Multi-Armed Bandits with Covariates.
Proceedings of the Algorithmic Learning Theory, 2018

2017
Dealing with under-reported variables: An information theoretic solution.
Int. J. Approx. Reasoning, 2017

Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid.
CoRR, 2017

Gradient Boosting Models for Photovoltaic Power Estimation Under Partial Shading Conditions.
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.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid.
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.
Proceedings of the Pattern Recognition and Image Analysis - 8th Iberian Conference, 2017

Boosting Java Performance Using GPGPUs.
Proceedings of the Architecture of Computing Systems - ARCS 2017, 2017

Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours.
Proceedings of the International Conference on Algorithmic Learning Theory, 2017

2016
Compiler-Driven Software Speculation for Thread-Level Parallelism.
ACM Trans. Program. Lang. Syst., 2016

Cost-sensitive boosting algorithms: Do we really need them?
Machine Learning, 2016

Ranking Biomarkers Through Mutual Information.
CoRR, 2016

Measuring the Stability of Feature Selection.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Estimating Mutual Information in Under-Reported Variables.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

2015
On unifiers, diversifiers, and the nature of pattern recognition.
Pattern Recognition Letters, 2015

Random Ordinality Ensembles: Ensemble methods for multi-valued categorical data.
Inf. Sci., 2015

Modular Autoencoders for Ensemble Feature Extraction.
CoRR, 2015

Boosting Java Performance using GPGPUs.
CoRR, 2015

General Terminology Induction in OWL.
Proceedings of the Semantic Web - ISWC 2015, 2015

Markov Blanket Discovery in Positive-Unlabelled and Semi-supervised Data.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

General Terminology Induction in OWL.
Proceedings of the Ontology Engineering, 2015

Modular Autoencoders for Ensemble Feature Extraction.
Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

Measuring the Stability of Feature Selection with Applications to Ensemble Methods.
Proceedings of the Multiple Classifier Systems - 12th International Workshop, 2015

Calibrating AdaBoost for Asymmetric Learning.
Proceedings of the Multiple Classifier Systems - 12th International Workshop, 2015

Is Feature Selection Secure against Training Data Poisoning?
Proceedings of the 32nd International Conference on Machine Learning, 2015

A scalable implementation of information theoretic feature selection for high dimensional data.
Proceedings of the 2015 IEEE International Conference on Big Data, 2015

2014
Random Projection Random Discretization Ensembles - Ensembles of Linear Multivariate Decision Trees.
IEEE Trans. Knowl. Data Eng., 2014

Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood.
Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2014

Statistical Hypothesis Testing in Positive Unlabelled Data.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Predicting Performance of OWL Reasoners: Locally or Globally?
Proceedings of the Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, 2014

Predicting OWL Reasoners: Locally or Globally?
Proceedings of the Informal Proceedings of the 27th International Workshop on Description Logics, 2014

2013
Optimizing software runtime systems for speculative parallelization.
TACO, 2013

Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications.
Journal of Machine Learning Research, 2013

ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge
CoRR, 2013

ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge.
Proceedings of the 7th International Workshop on Semantic Evaluation, 2013

Exploring sketches for probability estimation with sublinear memory.
Proceedings of the 2013 IEEE International Conference on Big Data, 2013

2012
Informative Priors for Markov Blanket Discovery.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection.
Journal of Machine Learning Research, 2012

2011
Garbage collection auto-tuning for Java mapreduce on multi-cores.
Proceedings of the 10th International Symposium on Memory Management, 2011

Accuracy exponentiation in UCS and its effect on voting margins.
Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Online, GA based mixture of experts: a probabilistic model of ucs.
Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Theoretical and empirical analysis of diversity in non-stationary learning.
Proceedings of the 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, 2011

2010
Ensemble Learning.
Proceedings of the Encyclopedia of Machine Learning, 2010

Learn++.MF: A random subspace approach for the missing feature problem.
Pattern Recognition, 2010

Fundamental Nano-Patterns to Characterize and Classify Java Methods.
Electr. Notes Theor. Comput. Sci., 2010

Online Non-stationary Boosting.
Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

"Good" and "Bad" Diversity in Majority Vote Ensembles.
Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

Some Thoughts at the Interface of Ensemble Methods and Feature Selection.
Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

The economics of garbage collection.
Proceedings of the 9th International Symposium on Memory Management, 2010

Toward a more accurate understanding of the limits of the TLS execution paradigm.
Proceedings of the 2010 IEEE International Symposium on Workload Characterization, 2010

Analytic Solutions to Differential Equations under Graph-Based Genetic Programming.
Proceedings of the Genetic Programming, 13th European Conference, 2010

2009
A New Perspective for Information Theoretic Feature Selection.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

A Study of Semi-supervised Generative Ensembles.
Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

An Information Theoretic Perspective on Multiple Classifier Systems.
Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

A Study of Random Linear Oracle Ensembles.
Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical Data.
Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

Modeling UCS as a mixture of experts.
Proceedings of the Genetic and Evolutionary Computation Conference, 2009

2007
Towards intelligent analysis techniques for object pretenuring.
Proceedings of the 5th International Symposium on Principles and Practice of Programming in Java, 2007

Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation.
Proceedings of the Multiple Classifier Systems, 7th International Workshop, 2007

Intelligent selection of application-specific garbage collectors.
Proceedings of the 6th International Symposium on Memory Management, 2007

Bayesian estimation of rule accuracy in UCS.
Proceedings of the Genetic and Evolutionary Computation Conference, 2007

UCSpv: principled voting in UCS rule populations.
Proceedings of the Genetic and Evolutionary Computation Conference, 2007

2006
Return Value Prediction meets Information Theory.
Electr. Notes Theor. Comput. Sci., 2006

2005
Managing Diversity in Regression Ensembles.
Journal of Machine Learning Research, 2005

Diversity creation methods: a survey and categorisation.
Information Fusion, 2005

Between Two Extremes: Examining Decompositions of the Ensemble Objective Function.
Proceedings of the Multiple Classifier Systems, 6th International Workshop, 2005

2004
Diversity in neural network ensembles.
PhD thesis, 2004

2003
Negative Correlation Learning and the Ambiguity Family of Ensemble Methods.
Proceedings of the Multiple Classifier Systems, 4th International Workshop, 2003

The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods.
Proceedings of the Machine Learning, 2003


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