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  • JOURNAL ARTICLE
    Chamberlain B, Levy-Kramer J, Humby C, Deisenroth MPet al., 2018,

    Real-time community detection in full social networks on a laptop

    , PLoS ONE, Vol: 13, ISSN: 1932-6203

    For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide

  • CONFERENCE PAPER
    Kamthe S, Deisenroth MP, 2018,

    Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control.

    , Artificial Intelligence and Statistics, Publisher: PMLR, Pages: 1701-1710
  • CONFERENCE PAPER
    Sæmundsson S, Hofmann K, Deisenroth MP, 2018,

    Meta Reinforcement Learning with Latent Variable Gaussian Processes.

    , Uncertainty in Artificial Intelligence
  • JOURNAL ARTICLE
    Arulkumaran K, Deisenroth MP, Brundage M, Bharath AAet al., 2017,

    A brief survey of deep reinforcement learning

    , IEEE Signal Processing Magazine, Vol: 34, Pages: 26-38, ISSN: 1053-5888

    Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude, we describe several current areas of research within the field.

  • CONFERENCE PAPER
    Chamberlain BP, Cardoso Â, Bryan Liu CH, Pagliari R, Deisenroth MPet al., 2017,

    Customer lifetime value prediction using embeddings

    , Pages: 1753-1762

    © 2017 Copyright held by the owner/author(s). We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.

  • CONFERENCE PAPER
    Chamberlain BP, Humby C, Deisenroth MP, 2017,

    Probabilistic Inference of Twitter Users' Age Based on What They Follow.

    , Publisher: Springer, Pages: 191-203
  • JOURNAL ARTICLE
    Creswell A, Bharath AA, 2017,

    Denoising Adversarial Autoencoders.

    , CoRR, Vol: abs/1703.01220
  • CONFERENCE PAPER
    Eleftheriadis S, Rudovic O, Deisenroth MP, Pantic Met al., 2017,

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

    , 13th Asian Conference on Computer Vision (ACCV), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 154-170, ISSN: 0302-9743
  • JOURNAL ARTICLE
    Eleftheriadis S, Rudovic O, Deisenroth MP, Pantic Met al., 2017,

    Gaussian Process Domain Experts for Modeling of Facial Affect

    , IEEE TRANSACTIONS ON IMAGE PROCESSING, Vol: 26, Pages: 4697-4711, ISSN: 1057-7149
  • CONFERENCE PAPER
    Huang R, Lattimore T, György A, Szepesvári Cet al., 2017,

    Following the leader and fast rates in online linear prediction: Curved constraint sets and other regularities

    , ISSN: 1532-4435

    © 2017 Ruitong Huang, Tor Lattimore, András György, and Csaba Szepesvári. Follow the leader (FTL) is a simple online learning algorithm that is known to perform well when the loss functions are convex and positively curved. In this paper we ask whether there are other settings when FTL achieves low regret. In particular, we study the fundamental problem of linear prediction over a convex, compact domain with non-empty interior. Amongst other results, we prove that the curvature of the boundary of the domain can act as if the losses were curved: In this case, we prove that as long as the mean of the loss vectors have positive lengths bounded away from zero, FTL enjoys logarithmic regret, while for polytope domains and stochastic data it enjoys finite expected regret. The former result is also extended to strongly convex domains by establishing an equivalence between the strong convexity of sets and the minimum curvature of their boundary, which may be of independent interest. Building on a previously known meta-algorithm, we also get an algorithm that simultaneously enjoys the worst-case guarantees and the smaller regret of FTL when the data is 'easy'. Finally, we show that such guarantees are achievable directly (e.g., by the follow the regularized leader algorithm or by a shrinkage-based variant of FTL) when the constraint set is an ellipsoid.

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