Papers etc

Lecture notes

  • • Bayesian Nonparametrics [PDF]
    • Machine Learning (Slides) [PDF]
    • Probability Theory II [PDF]


  • Quantifying the effects of data augmentation.
    With KH Huang and M Austern.
  • Subsampling large graphs and invariance in networks.
    P Orbanz.
    [arxiv] [A talk on this work]
  • Preferential attachment and vertex arrival times.
    With B Bloem-Reddy.
    [arxiv] [An article on inference in these models]
  • Independence by random scaling.
    With LF James.
  • Scaled subordinators and generalizations of the Indian buffet process.
    With LF James and YW Teh.
  • Publications

  • Limit theorems for distributions invariant under groups of transformations.
    With M Austern.
    Annals of Statistics, Vol. 50, No. 4, 1960–1991, 2022.

    [PDF] [Journal]
  • Uniform estimation in stochastic block models is slow.
    With I Castillo.
    Electronic Journal of Statistics, Vol. 16, No. 1, 2947-3000, 2022.

  • Empirical risk minimization and stochastic gradient descent for relational data.
    V Veitch, M Austern, W Zhou, DM Blei and P Orbanz.
    AISTATS 2019.

    [arxiv] [Code]
  • Distribution theory for hierarchical processes.
    With F Camerlenghi, A Lijoi and I Prünster.
    Annals of Statistics, Vol. 47, 67-92, 2019.

  • Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach
    W Zhou, V Veitch, M Austern, RP Adams and P Orbanz.
    ICLR 2019.

  • Random walk models of network formation and sequential Monte Carlo methods for graphs.
    B Bloem-Reddy and P Orbanz.
    Journal of the Royal Statistical Society, Series B, 80, 871-898, 2018.

    [Journal] [arxiv] [Code] [A talk on this work]
  • Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures."
    P Orbanz.
    [Discussion] Journal of the Royal Statistical Society, Series B, 79, Part 5.

  • Borel liftings of graph limits.
    With B Szegedy.
    Electronic Communications in Probability, Vol. 21, paper no. 65, 2016.

  • 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:
  • Nonparametric priors on complete separable metric spaces.
    P Orbanz.
  • Random function priors for exchangeable arrays with applications to graphs and relational data.
    JR Lloyd, P Orbanz, Z Ghahramani and DM Roy.
    Advances in Neural Information Processing Systems, 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.
  • Unit-rate Poisson representations of completely random measures.
    With S Williamson.
  • Dependent Indian Buffet Processes.
    S Williamson, P Orbanz and Z Ghahramani.
    AISTATS 2010, JMRL W&CP 9:924-931.
  • Conjugate Projective Limits.
    P Orbanz.
  • Bayesian Nonparametric Models.
    P Orbanz and YW Teh.
    In Encyclopedia of Machine Learning. Springer, 2010.
  • Construction of Nonparametric Bayesian Models from Parametric Bayes Equations.
    P Orbanz.
    Advances in Neural Information Processing Systems, 2009.
    [PDF]   [Supplements (Proofs)]
    [Techreport Version] (Identical text; proofs included as appendix)
  • Functional Conjugacy in Parametric Bayesian Models.
    P Orbanz.
    Techreport, 2009.
  • Music Preference Learning with Partial Information.
    Y Moh, P Orbanz and JM Buhmann.
    ICASSP 2008.
  • Nonparametric Bayesian Image Segmentation.
    P Orbanz and JM Buhmann.
    International Journal of Computer Vision (IJCV), Vol. 77, 25-45, 2008.
    [PDF]   [Journal]
  • 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]