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Konstantina Palla

non-parametric Bayesian methods and models

I am a postdoc at the Department of Statistics, University of Oxford, working with Prof. Yee Whye Teh. My research interests involve nonparametric Bayesian methods and models. I completed my Ph.D. in Machine Learning at the University of Cambridge and received a MA in Informatics from the University of Edinburgh.

Publications

2017

• K. Palla , D. Belgrave , A Birth-Death Modelling Framework for Inferring Disease Causality within the Context of Allergy Development., in 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017.
  
  Project: bigbayes

  @inproceedings{PallaBel17,
    author = {Palla, Konstantina and Belgrave, Danielle},
    title = {A Birth-Death Modelling Framework for Inferring Disease Causality within the Context of Allergy Development.},
    year = {2017},
    booktitle = {16th IEEE International Conference on Machine Learning and Applications (ICMLA)}
  }
  

• K. Palla , D. A. Knowles , Z. Ghahramani , A birth-death process for feature allocation., in Proceedings of the 34th International Conference on Machine Learning, 2017.
  
  Project: bigbayes

  We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birth-death feature allocation process (BDFP). The BDFP models the evolution of the feature allocation of a set of objects N across a covariate (e.g. time) by creating and deleting features. A BDFP is exchangeable, projective, stationary and reversible, and its equilibrium distribution is given by the Indian buffet process (IBP). We also present the Beta Event Process (BEP) and we show that it is the de Finetti mixing distribution underlying the BDFP. This results shows that the BEP plays the role for the BDFP that the Beta process plays for the Indian buffet process. Moreover, we show that the BEP permits simplified inference. The utility of this prior is demonstrated on synthetic and real world data.

  @inproceedings{PalKnoGha2016,
    author = {Palla, Konstantina and Knowles, David A. and Ghahramani, Zoubin},
    title = {A birth-death process for feature allocation.},
    booktitle = {Proceedings of the 34th International Conference on Machine Learning},
    year = {2017}
  }
  

2016

• K. Palla , F. Caron , Y. W. Teh , A Bayesian nonparametric model for sparse dynamic networks, Jun-2016.
  
  Project: bigbayes

  We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modeling the sociability of that node. Sociabilities are assumed to evolve over time, and are modeled via a dynamic point process model. The model is able to (a) capture smooth evolution of the interaction between nodes, allowing edges to appear/disappear over time (b) capture long term evolution of the sociabilities of the nodes (c) and yield sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying gamma process. We provide some theoretical insights into the model and apply it to three real world datasets.

  @unpublished{PallaCaronTeh2016,
    author = {Palla, Konstantina and Caron, Francois and Teh, Yee Whye},
    title = {A Bayesian nonparametric model for sparse dynamic networks},
    note = {ArXiv e-prints: 1607.01624},
    archiveprefix = {arXiv},
    year = {2016},
    month = jun
  }
  

• N. Heard , K. Palla , M. Skoularidou , Topic modelling of authentication events in an enterprise computer network, 2016.
  
  Project: bigbayes

  The possibility for theft or misuse of legitimate user credentials is a potential cyber-security weakness in any enterprise computer network which is almost impossible to eradicate. However, by monitoring the network traffic patterns, it can be possible to detect misuse of credentials. This article presents an initial investigation into deconvolving the mixture behaviour of several individuals within a network, to see if individual users can be identified. Towards that, a technique used for document classification is deployed, the Latent Dirichlet allocation model. A pilot study is conducted on authentication events taken from real data from the enterprise network of Los Alamos National Laboratory.

  @inproceedings{Heard:2016:10.1109/ISI.2016.7745466,
    author = {Heard, NA and Palla, K and Skoularidou, M},
    doi = {10.1109/ISI.2016.7745466},
    publisher = {IEEE},
    title = {Topic modelling of authentication events in an enterprise computer network},
    year = {2016}
  }
  

• K. Palla , F. Caron , Y. W. Teh , Bayesian Nonparametrics for Sparse Dynamic Networks, 2016.
  
  Project: bigbayes

  We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modeling the sociability of that node. Sociabilities are assumed to evolve over time, and are modeled via a dynamic point process model. The model is able to (a) capture smooth evolution of the interaction between nodes, allowing edges to appear/disappear over time (b) capture long term evolution of the sociabilities of the nodes (c) and yield sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying gamma process. We provide some theoretical insights into the model and apply it to three real world datasets.

  @unpublished{PalCarTeh2016a,
    author = {Palla, K. and Caron, F. and Teh, Y. W.},
    note = {ArXiv e-prints: 1607.01624},
    title = {Bayesian Nonparametrics for Sparse Dynamic Networks},
    year = {2016},
    bdsk-url-1 = {https://arxiv.org/pdf/1607.01624.pdf}
  }