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    Jahani E, Sundsøy P, Bjelland J, Bengtsson L, Pentland AS, de Montjoye Y-Aet al., 2017,

    Improving official statistics in emerging markets using machine learning and mobile phone data

    , EPJ Data Science, Vol: 6
    Joulani P, György A, Szepesvári C, 2017,

    A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds.

    , Publisher: PMLR, Pages: 681-720
    Kamthe S, Deisenroth MP, 2017,

    Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control.

    Kupcsik A, Deisenroth MP, Peters J, Loh AP, Vadakkepat P, Neumann Get al., 2017,

    Model-based contextual policy search for data-efficient generalization of robot skills

    , Artificial Intelligence, Vol: 247, Pages: 415-439, ISSN: 0004-3702

    © 2014 Elsevier B.V. In robotics, lower-level controllers are typically used to make the robot solve a specific task in a fixed context. For example, the lower-level controller can encode a hitting movement while the context defines the target coordinates to hit. However, in many learning problems the context may change between task executions. To adapt the policy to a new context, we utilize a hierarchical approach by learning an upper-level policy that generalizes the lower-level controllers to new contexts. A common approach to learn such upper-level policies is to use policy search. However, the majority of current contextual policy search approaches are model-free and require a high number of interactions with the robot and its environment. Model-based approaches are known to significantly reduce the amount of robot experiments, however, current model-based techniques cannot be applied straightforwardly to the problem of learning contextual upper-level policies. They rely on specific parametrizations of the policy and the reward function, which are often unrealistic in the contextual policy search formulation. In this paper, we propose a novel model-based contextual policy search algorithm that is able to generalize lower-level controllers, and is data-efficient. Our approach is based on learned probabilistic forward models and information theoretic policy search. Unlike current algorithms, our method does not require any assumption on the parametrization of the policy or the reward function. We show on complex simulated robotic tasks and in a real robot experiment that the proposed learning framework speeds up the learning process by up to two orders of magnitude in comparison to existing methods, while learning high quality policies.

    Olofsson S, Mehrian M, Geris L, Calandra R, Deisenroth MP, Misener Ret al., 2017,

    Bayesian Multi-Objective Optimisation of Neotissue Growth in a Perfusion Bioreactor Set-Up

    , Pages: 2155-2160, ISSN: 1570-7946

    © 2017 Elsevier B.V. We consider optimising bone neotissue growth in a 3D scaffold during dynamic perfusion bioreactor culture. The goal is to choose design variables by optimising two conflicting objectives: (i) maximising neotissue growth and (ii) minimising operating cost. Our contribution is a novel extension of Bayesian multi-objective optimisation to the case of one black-box (neotissue growth) and one analytical (operating cost) objective function, that helps determine, within a reasonable amount of time, what design variables best manage the trade-off between neotissue growth and operating cost. Our method is tested against and outperforms the most common approach in literature, genetic algorithms, and shows its important real-world applicability to problems that combine black-box models with easy-to-quantify objectives like cost.

    Somuyiwa SO, Gyorgy A, Gunduz D, 2017,

    Energy-Efficient Wireless Content Delivery with Proactive Caching

    , 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Publisher: IEEE
    Somuyiwa SO, Gyorgy A, Gunduz D, 2017,

    Improved Policy Representation and Policy Search for Proactive Content Caching in Wireless Networks

    , 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Publisher: IEEE
    Tiwari K, Honore V, Jeong S, Chong NY, Deisenroth MPet al., 2017,

    Resource-constrained decentralized active sensing for multi-robot systems using distributed Gaussian processes

    , Pages: 13-18, ISSN: 1598-7833

    © 2016 Institute of Control, Robotics and Systems - ICROS. We consider the problem of area coverage for robot teams operating under resource constraints, while modeling spatio-temporal environmental phenomena. The aim of the mobile robot team is to avoid exhaustive search and only visit the most important locations that can improve the prediction accuracy of a spatio-temporal model. We use a Gaussian Process (GP) to model spatially varying and temporally evolving dynamics of the target phenomenon. Each robot of the team is allocated a dedicated search area wherein the robot autonomously optimizes its prediction accuracy. We present this as a Decentralized Computation and Centralized Data Fusion approach wherein the trajectory sampled by the robot is generated using our proposed Resource-Constrained Decentralized Active Sensing (RC-DAS). Since each robot possesses its own independent prediction model, at the end of robot's mission time, we fuse all the prediction models from all robots to have a global model of the spatio-temporal phenomenon. Previously, all robots and GPs needed to be synchronized, such that the GPs can be jointly trained. However, doing so defeats the purpose of a fully decentralized mobile robot team. Thus, we allow the robots to independently gather new measurements and update their model parameters irrespective of other members of the team. To evaluate the performance of our model, we compare the trajectory traced by the robot using active and passive (e.g., nearest neighbor selection) sensing. We compare the performance and cost incurred by a resource constrained optimization with the unconstrained entropy maximization version.

    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.

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

    Classifying Options for Deep Reinforcement Learning.

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