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  • Journal article
    Palomeras N, Carrera A, Hurtós N, Karras GC, Bechlioulis CP, Cashmore M, Magazzeni D, Long D, Fox M, Kyriakopoulos KJ, Kormushev P, Salvi J, Carreras Met al., 2016,

    Toward persistent autonomous intervention in a subsea panel

    , Autonomous Robots, Vol: 40, Pages: 1279-1306
  • Journal article
    Filippi S, Holmes C, 2016,

    A Bayesian nonparametric approach to testing for dependence between random variables

    , Bayesian Analysis, Vol: 12, Pages: 919-938, ISSN: 1931-6690

    Nonparametric and nonlinear measures of statistical dependence between pairsof random variables are important tools in modern data analysis. In particularthe emergence of large data sets can now support the relaxation of linearityassumptions implicit in traditional association scores such as correlation.Here we describe a Bayesian nonparametric procedure that leads to a tractable,explicit and analytic quantification of the relative evidence for dependence vsindependence. Our approach uses Polya tree priors on the space of probabilitymeasures which can then be embedded within a decision theoretic test fordependence. Polya tree priors can accommodate known uncertainty in the form ofthe underlying sampling distribution and provides an explicit posteriorprobability measure of both dependence and independence. Well known advantagesof having an explicit probability measure include: easy comparison of evidenceacross different studies; encoding prior information; quantifying changes independence across different experimental conditions, and; the integration ofresults within formal decision analysis.

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

    Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs

    , 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Publisher: IEEE

    This 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.

  • Journal article
    Jamisola RS, Kormushev P, Roberts RG, Caldwell DGet al., 2016,

    Task-Space Modular Dynamics for Dual-Arms Expressed through a Relative Jacobian

    , Journal of Intelligent & Robotic Systems, Pages: 1-14, ISSN: 1573-0409
  • Conference paper
    Gyorgy A, Szepesvari C, 2016,

    Shifting Regret, Mirror Descent, and Matrices

    , International Conference on Machine Learning, Publisher: Journal of Machine Learning Research, Pages: 2943-2951, ISSN: 1532-4435

    We consider the problem of online prediction inchanging environments. In this framework theperformance of a predictor is evaluated as theloss relative to an arbitrarily changing predictor,whose individual components come from a baseclass of predictors. Typical results in the literatureconsider different base classes (experts, linearpredictors on the simplex, etc.) separately.Introducing an arbitrary mapping inside the mirrordecent algorithm, we provide a frameworkthat unifies and extends existing results. As anexample, we prove new shifting regret bounds formatrix prediction problems.

  • Conference paper
    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.

  • Conference paper
    Eleftheriadis S, Rudovic O, Deisenroth MP, Pantic Met al., 2016,

    Gaussian process domain experts for model adaptation in facial behavior analysis

    , Fourth International Workshop on Context Based Affect Recognition 2016, Publisher: IEEE
  • Journal article
    Ma ZB, Yang Y, Liu YX, Bharath AAet al., 2016,

    Recurrently decomposable 2-D convolvers for FPGA-based digital image processing

    , IEEE Transactions on Circuits and Systems, Vol: 63, Pages: 979-983, ISSN: 1549-7747

    Two-dimensional (2-D) convolution is a widely used operation in image processing and computer vision, characterized by intensive computation and frequent memory accesses. Previous efforts to improve the performance of field-programmable gate array (FPGA) convolvers focused on the design of buffering schemes and on minimizing the use of multipliers. A recently proposed recurrently decomposable (RD) filter design method can reduce the computational complexity of 2-D convolutions by splitting the convolution between an image and a large mask into a sequence of convolutions using several smaller masks. This brief explores how to efficiently implement RD based 2-D convolvers using FPGA. Three FPGA architectures are proposed based on RD filters, each with a different buffering scheme. The conclusion is that RD based architectures achieve higher area efficiency than other previously reported state-of-the-art methods, especially for larger convolution masks. An area efficiency metric is also suggested, which allows the most appropriate architecture to be selected.

  • Conference paper
    Joulani P, Gyorgy A, Szepesvari C, 2016,

    Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms

    , Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Publisher: AAAI

    We present a unified, black-box-style method for developingand analyzing online convex optimization (OCO) algorithmsfor full-information online learning in delayed-feedback environments.Our new, simplified analysis enables us to substantiallyimprove upon previous work and to solve a numberof open problems from the literature. Specifically, we developand analyze asynchronous AdaGrad-style algorithmsfrom the Follow-the-Regularized-Leader (FTRL) and MirrorDescentfamily that, unlike previous works, can handle projectionsand adapt both to the gradients and the delays, withoutrelying on either strong convexity or smoothness of theobjective function, or data sparsity. Our unified frameworkbuilds on a natural reduction from delayed-feedback to standard(non-delayed) online learning. This reduction, togetherwith recent unification results for OCO algorithms, allows usto analyze the regret of generic FTRL and Mirror-Descent algorithmsin the delayed-feedback setting in a unified mannerusing standard proof techniques. In addition, the reduction isexact and can be used to obtain both upper and lower boundson the regret in the delayed-feedback setting.

  • Book chapter
    Ahmadzadeh SR, Kormushev P, 2016,

    Visuospatial Skill Learning

    , Handling Uncertainty and Networked Structure in Robot Control, Editors: Busoniu, Tamás, Publisher: Springer International Publishing, Pages: 75-99, ISBN: 978-3-319-26327-4

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