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    Filippi 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: 1807-1828, ISSN: 1935-7524

    We present a novel Bayesian nonparametric regression model for covariates XX and continuous response variable Y∈RY∈R. The model is parametrized in terms of marginal distributions for YY and XX and a regression function which tunes the stochastic ordering of the conditional distributions F(y|x)F(y|x). By adopting an approximate composite likelihood approach, we show that the resulting posterior inference can be decoupled for the separate components of the model. This procedure can scale to very large datasets and allows for the use of standard, existing, software from Bayesian nonparametric density estimation and Plackett-Luce ranking estimation to be applied. As an illustration, we show an application of our approach to a US Census dataset, with over 1,300,000 data points and more than 100 covariates.

    Flaxman S, Sejdinovic D, Cunningham JP, Filippi Set al., 2016,

    Bayesian Learning of Kernel Embeddings

    , UAI'16

    Kernel methods are one of the mainstays of machine learning, but the problemof kernel learning remains challenging, with only a few heuristics and verylittle theory. This is of particular importance in methods based on estimationof kernel mean embeddings of probability measures. For characteristic kernels,which include most commonly used ones, the kernel mean embedding uniquelydetermines its probability measure, so it can be used to design a powerfulstatistical testing framework, which includes nonparametric two-sample andindependence tests. In practice, however, the performance of these tests can bevery sensitive to the choice of kernel and its lengthscale parameters. Toaddress this central issue, we propose a new probabilistic model for kernelmean embeddings, the Bayesian Kernel Embedding model, combining a Gaussianprocess prior over the Reproducing Kernel Hilbert Space containing the meanembedding with a conjugate likelihood function, thus yielding a closed formposterior over the mean embedding. The posterior mean of our model is closelyrelated to recently proposed shrinkage estimators for kernel mean embeddings,while the posterior uncertainty is a new, interesting feature with variouspossible applications. Critically for the purposes of kernel learning, ourmodel gives a simple, closed form marginal pseudolikelihood of the observeddata given the kernel hyperparameters. This marginal pseudolikelihood caneither be optimized to inform the hyperparameter choice or fully Bayesianinference can be used.

    Kurek M, Deisenroth MP, Luk W, Todman Tet al., 2016,

    Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs

    , 24th IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM), Publisher: IEEE, Pages: 84-87
    Ma Z-B, Yang Y, Liu Y-X, Bharath AAet al., 2016,

    Recurrently Decomposable 2-D Convolvers for FPGA-Based Digital Image Processing

    de 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: 1532-4435

    bandicoot 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

    Calandra R, Ivaldi S, Deisenroth MP, Peters Jet al., 2015,

    Learning torque control in presence of contacts using tactile sensing from robot skin

    , Pages: 690-695, ISSN: 2164-0572

    © 2015 IEEE. Whole-body control in unknown environments is challenging: Unforeseen contacts with obstacles can lead to poor tracking performance and potential physical damages of the robot. Hence, a whole-body control approach for future humanoid robots in (partially) unknown environments needs to take contact sensing into account, e.g., by means of artificial skin. However, translating contacts from skin measurements into physically well-understood quantities can be problematic as the exact position and strength of the contact needs to be converted into torques. In this paper, we suggest an alternative approach that directly learns the mapping from both skin and the joint state to torques. We propose to learn such an inverse dynamics models with contacts using a mixture-of-contacts approach that exploits the linear superimposition of contact forces. The learned model can, making use of uncalibrated tactile sensors, accurately predict the torques needed to compensate for the contact. As a result, tracking of trajectories with obstacles and tactile contact can be executed more accurately. We demonstrate on the humanoid robot iCub that our approach improve the tracking error in presence of dynamic contacts.

    Calandra R, Ivaldi S, Deisenroth MP, Rueckert E, Peters Jet al., 2015,

    Learning inverse dynamics models with contacts

    , Pages: 3186-3191, ISSN: 1050-4729

    © 2015 IEEE. In whole-body control, joint torques and external forces need to be estimated accurately. In principle, this can be done through pervasive joint-torque sensing and accurate system identification. However, these sensors are expensive and may not be integrated in all links. Moreover, the exact position of the contact must be known for a precise estimation. If contacts occur on the whole body, tactile sensors can estimate the contact location, but this requires a kinematic spatial calibration, which is prone to errors. Accumulating errors may have dramatic effects on the system identification. As an alternative to classical model-based approaches we propose a data-driven mixture-of-experts learning approach using Gaussian processes. This model predicts joint torques directly from raw data of tactile and force/torque sensors. We compare our approach to an analytic model-based approach on real world data recorded from the humanoid iCub. We show that the learned model accurately predicts the joint torques resulting from contact forces, is robust to changes in the environment and outperforms existing dynamic models that use of force/ torque sensor data.

    Deisenroth MP, Ng JW, 2015,

    Distributed Gaussian processes

    , Pages: 1481-1490

    Copyright © 2015 by the author(s). To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or variational parameters. The key idea is to recursively distribute computations to independent computational units and, subsequently, re-combine them to form an overall result. Efficient closed-form inference allows for straightforward parallelisation and distributed computations with a small memory footprint. The rBCM is independent of the computational graph and can be used on heterogeneous computing infrastructures, ranging from laptops to clusters. With sufficient computing resources our distributed GP model can handle arbitrarily large data sets.

    Deisenroth MP, Ng JW, 2015,

    Distributed Gaussian Processes

    , 2015 International Conference on Machine Learning (ICML), Publisher: Journal of Machine Learning Research

    To scale Gaussian processes (GPs) to large datasets we introduce the robust Bayesian CommitteeMachine (rBCM), a practical and scalableproduct-of-experts model for large-scaledistributed GP regression. Unlike state-of-theartsparse GP approximations, the rBCM is conceptuallysimple and does not rely on inducingor variational parameters. The key idea is torecursively distribute computations to independentcomputational units and, subsequently, recombinethem to form an overall result. Efficientclosed-form inference allows for straightforwardparallelisation and distributed computations witha small memory footprint. The rBCM is independentof the computational graph and canbe used on heterogeneous computing infrastructures,ranging from laptops to clusters. With sufficientcomputing resources our distributed GPmodel can handle arbitrarily large data sets.

    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Associating Locations Between Indoor Journeys from Wearable Cameras

    , 13th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 29-44, ISSN: 0302-9743

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