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    Calandra R, Peters J, Rasmussen CE, Deisenroth MPet al., 2016,

    Manifold Gaussian Processes for regression.

    , Publisher: IEEE, Pages: 3338-3345
    Calandra R, Seyfarth A, Peters J, Deisenroth MPet al., 2016,

    Bayesian optimization for learning gaits under uncertainty: An experimental comparison on a dynamic bipedal walker

    , Annals of Mathematics and Artificial Intelligence, Vol: 76, Pages: 5-23, ISSN: 1012-2443

    © 2015, Springer International Publishing Switzerland. Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parametrization, finding near-optimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and time-consuming design processes. Many different approaches for automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this article, we thoroughly discuss multiple automatic optimization methods in the context of gait optimization. We extensively evaluate Bayesian optimization, a model-based approach to black-box optimization under uncertainty, on both simulated problems and real robots. This evaluation demonstrates that Bayesian optimization is particularly suited for robotic applications, where it is crucial to find a good set of gait parameters in a small number of experiments.

    Creswell A, Bharath AA, 2016,

    Task Specific Adversarial Cost Function.

    , CoRR, Vol: abs/1609.08661
    Eleftheriadis S, Rudovic O, Deisenroth MP, Pantic Met al., 2016,

    Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis

    , 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Publisher: IEEE, Pages: 1469-1477, ISSN: 2160-7508
    Filippi S, Barnes CP, Kirk PDW, Kudo T, Kunida K, McMahon SS, Tsuchiya T, Wada T, Kuroda S, Stumpf MPHet al., 2016,

    Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling

    , CELL REPORTS, Vol: 15, Pages: 2524-2535, ISSN: 2211-1247
    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.

    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.

    Gyorgy A, Szcpesvari C, 2016,

    Shifting regret, mirror descent, and matrices

    , Pages: 4324-4332

    © 2016 by the author(s). We consider the problem of online prediction in changing environments. In this framework the performance of a predictor is evaluated as the loss relative to an arbitrarily changing predictor, whose individual components come from a base class of predictors. Typical results in the literature consider different base classes (experts, linear predictors on the simplex, etc.) separately. Introducing an arbitrary mapping inside the mirror decent algorithm, we provide a framework that unifies and extends existing results. As an example, we prove new shifting regret bounds for matrix prediction problems.

    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.

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