Publications
Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleZhang Q, Filippi S, Gretton A, et al., 2017,
Large-Scale Kernel Methods for Independence Testing
, Statistics and Computing, Vol: 28, Pages: 113-130, ISSN: 1573-1375Representations of probability measures in reproducing kernel Hilbert spacesprovide a flexible framework for fully nonparametric hypothesis tests ofindependence, which can capture any type of departure from independence,including nonlinear associations and multivariate interactions. However, theseapproaches come with an at least quadratic computational cost in the number ofobservations, which can be prohibitive in many applications. Arguably, it isexactly in such large-scale datasets that capturing any type of dependence isof interest, so striking a favourable tradeoff between computational efficiencyand test performance for kernel independence tests would have a direct impacton their applicability in practice. In this contribution, we provide anextensive study of the use of large-scale kernel approximations in the contextof independence testing, contrasting block-based, Nystrom and random Fourierfeature approaches. Through a variety of synthetic data experiments, it isdemonstrated that our novel large scale methods give comparable performancewith existing methods whilst using significantly less computation time andmemory.
-
Conference paperChamberlain BP, Humby C, Deisenroth MP, 2017,
Probabilistic Inference of Twitter Users' Age Based on What They Follow.
, Publisher: Springer, Pages: 191-203 -
Conference paperEleftheriadis S, Rudovic O, Deisenroth MP, et al., 2016,
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units
, 13th Asian Conference on Computer Vision (ACCV’16), Publisher: Springer, Pages: 154-170, ISSN: 0302-9743We address the task of simultaneous feature fusion and modelingof discrete ordinal outputs. We propose a novel Gaussian process(GP) auto-encoder modeling approach. In particular, we introduce GPencoders to project multiple observed features onto a latent space, whileGP decoders are responsible for reconstructing the original features. Inferenceis performed in a novel variational framework, where the recoveredlatent representations are further constrained by the ordinal outputlabels. In this way, we seamlessly integrate the ordinal structure in thelearned manifold, while attaining robust fusion of the input features.We demonstrate the representation abilities of our model on benchmarkdatasets from machine learning and affect analysis. We further evaluatethe model on the tasks of feature fusion and joint ordinal predictionof facial action units. Our experiments demonstrate the benefits of theproposed approach compared to the state of the art.
-
Conference paperEleftheriadis S, Rudovic O, Deisenroth MP, et al., 2016,
Gaussian process domain experts for model adaptation in facial behavior analysis
, Fourth International Workshop on Context Based Affect Recognition 2016, Publisher: IEEE, Pages: 1469-1477 -
Journal articleFilippi S, Holmes CC, Nieto-Barajas LE, 2016,
Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures
, Electronic Journal of Statistics, Vol: 10, Pages: 3338-3354, ISSN: 1935-7524In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a “null model” of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.
-
Conference paperCalandra R, Peters J, Rasmussen CE, et al., 2016,
Manifold Gaussian Processes for Regression
, International Joint Conference on Neural Networks, Publisher: IEEE, ISSN: 2161-4407Off-the-shelf Gaussian Process (GP) covariancefunctions encode smoothness assumptions on the structureof the function to be modeled. To model complex and nondifferentiablefunctions, these smoothness assumptions are oftentoo restrictive. One way to alleviate this limitation is to finda different representation of the data by introducing a featurespace. This feature space is often learned in an unsupervisedway, which might lead to data representations that are notuseful for the overall regression task. In this paper, we proposeManifold Gaussian Processes, a novel supervised method thatjointly learns a transformation of the data into a featurespace and a GP regression from the feature space to observedspace. The Manifold GP is a full GP and allows to learn datarepresentations, which are useful for the overall regressiontask. As a proof-of-concept, we evaluate our approach oncomplex non-smooth functions where standard GPs performpoorly, such as step functions and robotics tasks with contacts.
-
Journal articlede Montjoye YKJV, Rocher L, Pentland AS, 2016,
bandicoot: an open-source Python toolbox to analyze mobile phone metadata
, Journal of Machine Learning Research, Vol: 17, ISSN: 1532-4435bandicoot is an open-source Python toolbox to extract more than 1442 features from standard mobile phone metadata. bandicoot makes it easy for machine learning researchers and practitioners to load mobile phone data, to analyze and visualize them, and to extract robust features which can be used for various classification and clustering tasks. Emphasis is put on ease of use, consistency, and documentation. bandicoot has no dependencies and is distributed under MIT license
-
Journal articlePalomeras N, Carrera A, Hurtós N, et al., 2016,
Toward persistent autonomous intervention in a subsea panel
, Autonomous Robots, Vol: 40, Pages: 1279-1306 -
Journal articleCreswell A, Bharath AA, 2016,
Task Specific Adversarial Cost Function
The cost function used to train a generative model should fit the purpose ofthe model. If the model is intended for tasks such as generating perceptuallycorrect samples, it is beneficial to maximise the likelihood of a sample drawnfrom the model, Q, coming from the same distribution as the training data, P.This is equivalent to minimising the Kullback-Leibler (KL) distance, KL[Q||P].However, if the model is intended for tasks such as retrieval or classificationit is beneficial to maximise the likelihood that a sample drawn from thetraining data is captured by the model, equivalent to minimising KL[P||Q]. Thecost function used in adversarial training optimises the Jensen-Shannon entropywhich can be seen as an even interpolation between KL[Q||P] and KL[P||Q]. Here,we propose an alternative adversarial cost function which allows easy tuning ofthe model for either task. Our task specific cost function is evaluated on adataset of hand-written characters in the following tasks: Generation,retrieval and one-shot learning.
-
Conference paperKurek M, Deisenroth MP, Luk W, et al., 2016,
Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs
, 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Publisher: IEEEThis paper presents a novel approach for automatic optimisation of reconfigurable design parameters based on knowledge transfer. The key idea is to make use of insights derived from optimising related designs to benefit future optimisations. We show how to use designs targeting one device to speed up optimisation of another device. The proposed approach is evaluated based on various applications including computational finance and seismic imaging. It is capable of achieving up to 35% reduction in optimisation time in producing designs with similar performance, compared to alternative optimisation methods.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.