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29th International Conference on machine learning, ICML 2012

作者： Bracegirdle, Chris Barber, David Centre for Computational Statistics and Machine Learning University College London Gower Street London United Kingdom

ISBN: (纸本)9781450312851

Cointegration is an important topic for time-series, and describes a relationship between two series in which a linear combination is stationary. Classically, the test for cointegration is based on a two stage process in which first the linear relation between the series is estimated by Ordinary Least Squares. Subsequently a unit root test is performed on the residuals. A well-known deficiency of this classical approach is that it can lead to erroneous conclusions about the presence of cointegration. As an alternative, we present a framework for estimating whether cointegration exists using Bayesian inference which is empirically superior to the classical approach. Finally, we apply our technique to model segmented cointegration in which cointegration may exist only for limited time. In contrast to previous approaches our model makes no restriction on the number of possible cointegration segments. Copyright 2012 by the author(s)/owner(s).

关键词： Bayesian networks

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Improved loss bounds for multiple kernel learning

Improved loss bounds for multiple kernel learning

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14th International Conference on Artificial Intelligence and statistics, AISTATS 2011

作者： Hussain, Zakria Shawe-Taylor, John Centre for Computational Statistics and Machine Learning Department of Computer Science University College London United Kingdom

We propose two new generalization error bounds for multiple kernel learning (MKL). First, using the bound of Srebro and Ben-David (2006) as a starting point, we derive a new version which uses a simple counting argument for the choice of kernels in order to generate a tighter bound when 1-norm regularization (sparsity) is imposed in the kernel learning problem. The second bound is a Rademacher complexity bound which is additive in the (logarithmic) kernel complexity and margin term. This dependence is superior to all previously published Rademacher bounds for learning a convex combination of kernels, including the recent bound of Cortes et al. (2010), which exhibits a multiplicative interaction. We illustrate the tightness of our bounds with simulations. Copyright 2011 by the authors.

关键词： Artificial intelligence

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Observational-interventional priors for dose-response learning 30

Observational-interventional priors for dose-response learni...

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30th Annual Conference on Neural Information Processing Systems, NIPS 2016

作者： Silva, Ricardo Department of Statistical Science Centre for Computational Statistics and Machine Learning University College London United Kingdom

Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treatment is not controlled by a known mechanism, is sometimes available. Under some strong assumptions, observational data allows for the estimation of dose-response curves. Estimating such curves nonparametrically is hard: sample sizes for controlled interventions may be small, while in the observational case a large number of measured confounders may need to be marginalized. In this paper, we introduce a hierarchical Gaussian process prior that constructs a distribution over the dose-response curve by learning from observational data, and reshapes the distribution with a nonparametric affine transform learned from controlled interventions. This function composition from different sources is shown to speed-up learning, which we demonstrate with a thorough sensitivity analysis and an application to modeling the effect of therapy on cognitive skills of premature infants. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Affine transforms

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Matching pursuit kernel fisher discriminant analysis

Matching pursuit kernel fisher discriminant analysis

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12th International Conference on Artificial Intelligence and statistics, AISTATS 2009

作者： Diethe, Tom Hussain, Zakria Hardoon, David R. Shawe-Taylor, John Department of Computer Science Centre for Computational Statistics and Machine Learning University College London WC1E 6BT United Kingdom

We derive a novel sparse version of Kernel Fisher Discriminant Analysis (KFDA) using an approach based on Matching Pursuit (MP). We call this algorithm Matching Pursuit Kernel Fisher Discriminant Analysis (MPKFDA). We provide generalisation error bounds analogous to those constructed for the Robust Minimax algorithm together with a sample compression bounding technique. We present experimental results on real world datasets, which show that MPKFDA is competitive with the KFDA and the SVM on UCI datasets, and additional experiments that show that the MPKFDA on average outperforms KFDA and SVM in extremely high dimensional settings. © 2009 by the authors.

关键词： Discriminant analysis

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PAC-Bayes analysis of maximum entropy learning

PAC-Bayes analysis of maximum entropy learning

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12th International Conference on Artificial Intelligence and statistics, AISTATS 2009

作者： Shawe-Taylor, John Hardoon, David R. Department of Computer Science Centre for Computational Statistics and Machine Learning University College London WC1E 6BT United Kingdom

We extend and apply the PAC-Bayes theorem to the analysis of maximum entropy learning by considering maximum entropy classification. The theory introduces a multiple sampling technique that controls an effective margin of the bound. We further develop a dual implementation of the convex optimisation that optimises the bound. This algorithm is tested on some simple datasets and the value of the bound compared with the test error.© 2009 by the authors.

关键词： Convex optimization

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A new feature selection method based on stability theory - Exploring parameters space to evaluate classification accuracy in neuroimaging data

A new feature selection method based on stability theory - E...

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International Workshop on machine learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011

作者： Rondina, Jane M. Shawe-Taylor, John Mourão-Miranda, Janaina Centre for Neuroimaging Sciences Institute of Psychiatry King's College London United Kingdom Department of Computer Science Centre for Computational Statistics and Machine Learning University College London United Kingdom

Recently we proposed a feature selection method based on stability theory. In the present work we present an evaluation of its performance in different contexts through a grid search performed in a subset of its parameters space. The main contributions of this work are: we show that the method can improve the classification accuracy in relation to the wholebrain in different functional datasets;we evaluate the parameters influence in the results, getting some insight in reasonable ranges of values;and we show that combinations of parameters that yield the best accuracies are stable (i.e., they have low rates of false positive selections). © 2012 Springer-Verlag.

关键词： Neuroimaging

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Data mining, data fusion and information management

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IEE Proceedings: Intelligent Transport Systems 2006年第3期153卷 221-229页

作者： Shawe-Taylor, J. De Bie, T. Cristianini, N. Centre for Computational Statistics and Machine Learning University College London Gower Street London WC1E 6BT United Kingdom Department of Engineering Mathematics University of Bristol University Walk Bristol BS8 1TR United Kingdom

The potential impact of advances in data mining, data fusion and information management on the efficient exploitation of the transport infrastructure are addressed. It is argued that the energy currently consumed in transport is poorly exploited for a number of reasons that the application of intelligent infrastructure analysis can significantly address: traffic congestion implies reduced fuel efficiency;the average number of people travelling per car is very low;and public/private transport combination options are difficult to plan. Relevant advances in intelligent information systems are reviewed, and a number of scenarios illustrating how these weaknesses might be addressed are presented. For each, the feasibility of the required technology and the timescales for deployment are discussed.

关键词： Data mining

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Observational-Interventional Priors for Dose-Response learning 16

Observational-Interventional Priors for Dose-Response Learni...

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Annual Conference on Neural Information Processing Systems

作者： Ricardo Silva Department of Statistical Science and Centre for Computational Statistics and Machine Learning University College London

ISBN: (纸本)9781510838819

关键词： response curves Information source observational data Dose-response relationship learning Affine transformation Sensitivity analysis Curves Intervention Premature Infant Standing Orders

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An inequality with applications to structured sparsity and multitask dictionary learning 27

An inequality with applications to structured sparsity and m...

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27th Conference on learning Theory, COLT 2014

作者： Maurer, Andreas Pontil, Massimiliano Romera-Paredes, Bernardino Adalbertstrasse 55 MunchenD-80799 Germany Department of Computer Science Centre for Computational Statistics and Machine Learning University College London United Kingdom Department of Computer Science UCL Interactive Centre University College London United Kingdom

From concentration inequalities for the suprema of Gaussian or Rademacher processes an inequality is derived. It is applied to sharpen existing and to derive novel bounds on the empirical Rademacher complexities of unit balls in various norms appearing in the context of structured sparsity and multitask dictionary learning or matrix factorization. A key role is played by the largest eigenvalue of the data covariance matrix. © 2014 A. Maurer, M. Pontil & B. Romera-Paredes.

关键词： Covariance matrix

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A Bayesian approach to approximate joint diagonalization of square matrices

A Bayesian approach to approximate joint diagonalization of ...

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29th International Conference on machine learning, ICML 2012

作者： Zhong, Mingjun Girolami, Mark Department of Biomedical Engineering Dalian University of Technology Dalian 116023 China Department of Statistical Science Centre for Computational Statistics and Machine Learning University College London London WCIE 7HB United Kingdom

ISBN: (纸本)9781450312851

We present a Bayesian scheme for the approximate diagonalisation of several square matrices which are not necessarily symmetric. A Gibbs sampler is derived to simulate samples of the common eigenvectors and the eigenvalues for these matrices. Several synthetic examples are used to illustrate the performance of the proposed Gibbs sampler and we then provide comparisons to several other joint diagonalization algorithms, which shows that the Gibbs sampler achieves the state-of-the- art performance on the examples considered. As a byproduct, the output of the Gibbs sampler could be used to estimate the log marginal likelihood, however we employ the approximation based on the Bayesian information criterion (BIC) which in the synthetic examples considered correctly located the number of common eigenvectors. We then succesfully applied the sampler to the source separation problem as well as the common principal component analysis and the common spatial pattern analysis problems. Copyright 2012 by the author(s)/owner(s).

关键词： Eigenvalues and eigenfunctions

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