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- Mauricio A. Álvarez, David Luengo, Neil D. Lawrence
- AISTATS
- 2009

Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how… (More)

- Jesse Read, Luca Martino, David Luengo
- Pattern Recognition
- 2014

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense… (More)

- Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence
- AISTATS
- 2010

Interest in multioutput kernel methods is increasing, whether under the guise of multitask learning, multisensor networks or structured output data. From the Gaussian process perspective a multioutput Mercer kernel is a covariance function over correlated output functions. One way of constructing such kernels is based on convolution processes (CP). A key… (More)

- Mauricio A. Álvarez, David Luengo, Neil D. Lawrence
- IEEE Transactions on Pattern Analysis and Machine…
- 2013

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how… (More)

- Jesse Read, Luca Martino, David Luengo
- 2013 IEEE International Conference on Acoustics…
- 2013

Multi-label classification (MLC) is the supervised learning problem where an instance may be associated with multiple labels. Modeling dependencies between labels allows MLC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies. On the… (More)

- Jesse Read, Luca Martino, Pablo M. Olmos, David Luengo
- Pattern Recognition
- 2015

Multi-output inference tasks, such as multi-label classification, have become increasingly important in recent years. A popular method for multi-label classification is classifier chains, in which the predictions of individual classifiers are cascaded along a chain, thus taking into account inter-label dependencies and improving the overall performance.… (More)

- David Luengo, Luca Martino
- 2013 IEEE International Conference on Acoustics…
- 2013

Markov Chain Monte Carlo methods are widely used in signal processing and communications for statistical inference and stochastic optimization. In this work, we introduce an efficient adaptive Metropolis-Hastings algorithm to draw samples from generic multimodal and multidimensional target distributions. The proposal density is a mixture of Gaussian… (More)

- David Luengo, Ignacio Santamaría, Luis Vielva
- Neurocomputing
- 2005

ification; In this paper, we present a computationally efficient algorithm which provides a solution to blind inverse problems for sparse input signals. The method takes advantag clustering typical of sparse input signals to identify the channel matrix, solving four p sequentially: detecting the number of input signals (i.e. clusters), estimating the direc… (More)

- Luca Martino, Jesse Read, David Luengo
- IEEE Transactions on Signal Processing
- 2015

Bayesian methods have become very popular in signal processing lately, even though performing exact Bayesian inference is often unfeasible due to the lack of analytical expressions for optimal Bayesian estimators. In order to overcome this problem, Monte Carlo (MC) techniques are frequently used. Several classes of MC schemes have been developed, including… (More)

- Victor Elvira, Luca Martino, David Luengo, Mónica F. Bugallo
- ACSSC
- 2015