Papers etc

  • Lecture notes
    • Bayesian Nonparametrics [PDF]
    • Machine Learning (Slides) [PDF]
    • Probability Theory II [PDF]
  • Uniform estimation of a class of random graph functionals.
    With I Castillo.
    Submitted.

    [arxiv]
  • Independence by random scaling.
    With LF James.
    Submitted.

    [arxiv]
  • Random walk models of network formation and sequential Monte Carlo methods for graphs.
    B Bloem-Reddy and P Orbanz.
    Submitted.

    [arxiv] [A talk on this work]
  • Scaled subordinators and generalizations of the Indian buffet process.
    With LF James and YW Teh.
    Submitted.

    [arxiv]
  • Hierarchical processes and prediction.
    With F Camerlenghi, A Lijoi and I Prünster.
    Submitted.

  • Unit-rate Poisson representations of completely random measures.
    With S Williamson.
    [PDF]
  • Borel liftings of graph limits.
    With B Szegedy.
    Electronic Communications in Probability, Vol. 21, paper no. 65, 2016.

    [PDF] [arxiv]
  • Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures.
    With DM Roy.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, 437-461, 2015.

    [Journal] [arxiv]
    Some of the key ideas described in this paper are also explained in the following talk:
    [Video]
  • Nonparametric priors on complete separable metric spaces.
    P Orbanz.
    [PDF]
  • Random function priors for exchangeable arrays with applications to graphs and relational data.
    JR Lloyd, P Orbanz, Z Ghahramani and DM Roy.
    NIPS 2012.
    [PDF]   [Talk on this and related work (Video, 50min)]
  • Projective Limit Random Probabilities on Polish Spaces.
    P Orbanz.
    Electronic Journal of Statistics, Vol. 5, 1354-1373, 2011.
    [PDF]
  • Dependent Indian Buffet Processes.
    S Williamson, P Orbanz and Z Ghahramani.
    AISTATS 2010, JMRL W&CP 9:924-931.
    [PDF]
  • Conjugate Projective Limits.
    P Orbanz.
    [arxiv]
  • Bayesian Nonparametric Models.
    P Orbanz and YW Teh.
    In Encyclopedia of Machine Learning. Springer, 2010.
    [PDF]
  • Construction of Nonparametric Bayesian Models from Parametric Bayes Equations.
    P Orbanz.
    NIPS 2009.
    [PDF]   [Supplements (Proofs)]
    [Techreport Version] (Identical text; proofs included as appendix)
  • Functional Conjugacy in Parametric Bayesian Models.
    P Orbanz.
    Techreport, 2009.
    [PDF]
  • Music Preference Learning with Partial Information.
    Y Moh, P Orbanz and JM Buhmann.
    ICASSP 2008.
    [PDF]
  • Nonparametric Bayesian Image Segmentation.
    P Orbanz and JM Buhmann.
    International Journal of Computer Vision (IJCV), Vol. 77, 25-45, 2008.
    [PDF]   [Journal]   [Code]
  • Cluster Analysis of Heterogeneuos Rank Data.
    LM Busse, P Orbanz and JM Buhmann.
    International Conference on Machine Learning (ICML), 2007.
    [PDF]   [PDF (with corrections)]
  • Bayesian Order-Adaptive Clustering for Video Segmentation.
    P Orbanz, S Braendle and JM Buhmann.
    EMMCVPR, 2007.
    [PDF]   [Publisher]
  • Smooth Image Segmentation by Nonparametric Bayesian Inference.
    P Orbanz and JM Buhmann.
    European Conference on Computer Vision (ECCV), Vol. 1, 444-457, 2006.
    [PDF]   [Publisher]
  • SAR Images as Mixtures of Gaussian Mixtures.
    P Orbanz and JM Buhmann.
    IEEE International Conference on Image Processing (ICIP), Vol. 2, 209-212, 2005.
    [PDF]   [Publisher]