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  • JOURNAL ARTICLE
    Zhang Q, Filippi S, Gretton A, Sejdinovic Det al., 2017,

    Large-Scale Kernel Methods for Independence Testing

    , Statistics and Computing, Vol: 28, Pages: 113-130, ISSN: 1573-1375

    Representations 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 PAPER
    Zhang Q, Filippi SL, Flaxman S, Sejdinovic Det al., 2017,

    Feature-to-feature regression for a two-step conditional independence test

    , Uncertainty in Artificial Intelligence

    The algorithms for causal discovery and morebroadly for learning the structure of graphicalmodels require well calibrated and consistentconditional independence (CI) tests. We revisitthe CI tests which are based on two-step proceduresand involve regression with subsequent(unconditional) independence test (RESIT) onregression residuals and investigate the assumptionsunder which these tests operate. In particular,we demonstrate that when going beyond simplefunctional relationships with additive noise,such tests can lead to an inflated number of falsediscoveries. We study the relationship of thesetests with those based on dependence measuresusing reproducing kernel Hilbert spaces (RKHS)and propose an extension of RESIT which usesRKHS-valued regression. The resulting test inheritsthe simple two-step testing procedure ofRESIT, while giving correct Type I control andcompetitive power. When used as a componentof the PC algorithm, the proposed test is morerobust to the case where hidden variables inducea switching behaviour in the associations presentin the data.

  • BOOK CHAPTER
    Ahmadzadeh SR, Mastrogiovanni F, Kormushev P, 2016,

    Visuospatial Skill Learning for Robots

    A novel skill learning approach is proposed that allows a robot to acquirehuman-like visuospatial skills for object manipulation tasks. Visuospatialskills are attained by observing spatial relationships among objects throughdemonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-basedapproach that focuses on achieving a desired goal configuration of objectsrelative to one another while maintaining the sequence of operations. VSL iscapable of learning and generalizing multi-operation skills from a singledemonstration, while requiring minimum prior knowledge about the objects andthe environment. In contrast to many existing approaches, VSL offerssimplicity, efficiency and user-friendly human-robot interaction. We also showthat VSL can be easily extended towards 3D object manipulation tasks, simply byemploying point cloud processing techniques. In addition, a robot learningframework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and aconventional planning method. In VSL-SP, the sequence of performed actions arelearned using VSL, while the sensorimotor skills are learned using aconventional trajectory-based learning approach. such integration easilyextends robot capabilities to novel situations, even by users withoutprogramming ability. In VSL-SP the internal planner of VSL is integrated withan existing action-level symbolic planner. Using the underlying constraints ofthe task and extracted symbolic predicates, identified by VSL, symbolicrepresentation of the task is updated. Therefore the planner maintains ageneralized representation of each skill as a reusable action, which can beused in planning and performed independently during the learning phase. Theproposed approach is validated through several real-world experiments.

  • CONFERENCE PAPER
    Arulkumaran K, Dilokthanakul N, Shanahan M, Bharath AAet al., 2016,

    Classifying Options for Deep Reinforcement Learning.

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

    Manifold Gaussian Processes for regression.

    , Publisher: IEEE, Pages: 3338-3345
  • JOURNAL ARTICLE
    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.

  • JOURNAL ARTICLE
    Creswell A, Bharath AA, 2016,

    Task Specific Adversarial Cost Function.

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

    Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units.

    , Pages: 154-170
  • JOURNAL ARTICLE
    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

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