Search or filter publications

Filter by type:

Filter by publication type

Filter by year:

to

Results

  • Showing results for:
  • Reset all filters

Search results

  • 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

  • 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 B, Liu CHB, Cardoso A, Pagliari R, Deisenroth MPet al., 2017,

    Customer life time value prediction using embeddings

    , 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Publisher: ACM

    We describe the Customer Life Time Value (CLTV) prediction sys-tem deployed at ASOS.com, a global online fashion retailer. CLTVprediction is an important problem in e-commerce where an accu-rate estimate of future value allows retailers to effectively allocatemarketing spend, identify and nurture high value customers andmitigate exposure to losses.The system at ASOS provides dailyestimates of the future value of every customer and is one of thecornerstones of the personalised shopping experience. The state ofthe art in this domain uses large numbers of handcrafted featuresand ensemble regressors to forecast value, predict churn and evalu-ate customer loyalty. We describe our system, which adopts thisapproach, and our ongoing e‚orts to further improve it. Recently,domains including language, vision and speech have shown dra-matic advances by replacing hand-crafted features with featuresthat are learned automatically from data. We show that learningfeature representations is a promising extension to the state of theart in CLTV modeling. We propose a novel way to generate embed-dings of customers which addresses the issue of the ever changingproduct catalogue and obtain a signi€cant improvement over anexhaustive set of handcrafted features.

  • CONFERENCE PAPER
    Chamberlain BP, Cardoso A, Liu CHB, Pagliari R, Deisenroth MPet al., 2017,

    Customer Lifetime Value Prediction Using Embeddings

    , International Conference on Knowledge Discovery and Data Mining, Publisher: ACM, Pages: 1753-1762

    We describe the Customer LifeTime Value (CLTV) prediction system deployed atASOS.com, a global online fashion retailer. CLTV prediction is an importantproblem in e-commerce where an accurate estimate of future value allowsretailers to effectively allocate marketing spend, identify and nurture highvalue customers and mitigate exposure to losses. The system at ASOS providesdaily estimates of the future value of every customer and is one of thecornerstones of the personalised shopping experience. The state of the art inthis domain uses large numbers of handcrafted features and ensemble regressorsto forecast value, predict churn and evaluate customer loyalty. Recently,domains including language, vision and speech have shown dramatic advances byreplacing handcrafted features with features that are learned automaticallyfrom data. We detail the system deployed at ASOS and show that learning featurerepresentations is a promising extension to the state of the art in CLTVmodelling. We propose a novel way to generate embeddings of customers, whichaddresses the issue of the ever changing product catalogue and obtain asignificant improvement over an exhaustive set of handcrafted features.

  • CONFERENCE PAPER
    Eleftheriadis S, Rudovic O, Deisenroth MP, Pantic Met al., 2017,

    Variational gaussian process auto-Encoder for ordinal prediction of facial action units

    , Pages: 154-170, ISSN: 0302-9743

    © Springer International Publishing AG 2017. We address the task of simultaneous feature fusion and modeling of discrete ordinal outputs. We propose a novel Gaussian process (GP) autoencoder modeling approach. In particular, we introduce GP encoders to project multiple observed features onto a latent space, while GP decoders are responsible for reconstructing the original features. Inference is performed in a novel variational framework, where the recovered latent representations are further constrained by the ordinal output labels. In this way, we seamlessly integrate the ordinal structure in the learned manifold, while attaining robust fusion of the input features. We demonstrate the representation abilities of our model on benchmark datasets from machine learning and affect analysis. We further evaluate the model on the tasks of feature fusion and joint ordinal prediction of facial action units. Our experiments demonstrate the benefits of the proposed approach compared to the state of the art.

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

    Feature-to-Feature Regression for a Two-Step Conditional Independence Test

    , Uncertainty in Artificial Intelligence
  • 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.

  • JOURNAL ARTICLE
    Huang R, Lattimore T, Gyorgy A, Szepesvari Cet al., 2017,

    Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities

    , Journal of Machine Learning Research, Vol: 18(145), Pages: 1-31, ISSN: 1532-4435

    Follow the leader (FTL) is a simple online learning algorithm that is known to perform well whenthe loss functions are convex and positively curved. In this paper we ask whether there are othersettings when FTL achieves low regret. In particular, we study the fundamental problem of linearprediction over a convex, compact domain with non-empty interior. Amongst other results, weprove that the curvature of the boundary of the domain can act as if the losses were curved: In thiscase, we prove that as long as the mean of the loss vectors have positive lengths bounded away fromzero, FTL enjoys logarithmic regret, while for polytope domains and stochastic data it enjoys finiteexpected regret. The former result is also extended to strongly convex domains by establishing anequivalence 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 alsoget an algorithm that simultaneously enjoys the worst-case guarantees and the smaller regret ofFTL 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 theconstraint set is an ellipsoid.

  • JOURNAL ARTICLE
    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

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=954&limit=10&respub-action=search.html Current Millis: 1519332117492 Current Time: Thu Feb 22 20:41:57 GMT 2018