Peter Orbanz

Welcome.

I'm a Research Fellow (EPSRC Mathematical Sciences) at the University of Cambridge, where I am based in the Machine Learning Group of Zoubin Ghahramani. I am a member of St John's College.

I hold a degree in computer science and mathematics from the University of Bonn, and a PhD from ETH Zurich, where I worked with Joachim M. Buhmann.

I will join the Statistics Faculty at Columbia University in Summer 2012.

Tutorials on Bayesian nonparametrics

I have given a number of tutorials at NIPS 2011 (with Yee Whye Teh) and at Machine Learning Summer Schools.

Please see my tutorial page for slides, video recordings and further reading.

Research

My main research interest are the statistics of discrete objects and structures: permutations, graphs, partitions, binary sequences. Most of my recent work concerns representation problems and latent variable algorithms in Bayesian nonparametrics. More generally, I am interested in all mathematical aspects of machine learning and artifical intelligence.

Working Papers

Unit-Rate Poisson Representations of Completely Random Measures.
P Orbanz and S Williamson.
[PDF]

Nonparametric priors on complete separable metric spaces.
P Orbanz.
[PDF]

Publications

Projective Limit Random Probabilities on Polish Spaces.
P Orbanz.
Electronic Journal of Statistics, Vol. 5, 1354-1373, 2011.
[PDF (arXiv)]

Dependent Indian Buffet Processes.
S Williamson, P Orbanz and Z Ghahramani.
AISTATS 2010, JMRL W&CP 9:924-931.
[PDF]

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)

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] [Publisher] [Code]

Cluster Analysis of Heterogeneuos Rank Data.
LM Busse, P Orbanz and JM Buhmann.
International Conference on Machine Learning (ICML), 2007.
[PDF (with corrections)]     [PDF (as published)]

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.
[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]

Notes and Techreports

Conjugate Projective Limits.
P Orbanz. [PDF (arXiv)]

Functional Conjugacy in Parametric Bayesian Models.
P Orbanz, 2009. [PDF]

PhD Thesis

Infinite-Dimensional Exponential Families in the Cluster Analysis of Structured Data.
ETH Zurich, 2008.
[PDF]

Talks

Nonparametric Bayes tutorials (various talks).
[Tutorial page]

Some other recent talks:

Projective limit techniques in Bayesian nonparametrics.
[Slides]

Exchangeability, symmetry, and sufficiency.
[Slides]

Partition priors in computer vision.
[Slides]

Machine Learning Summer School 2009

We organized the the Machine Learning Summer School 2009 here in Cambridge. All talks are available on Videolectures.

Iain Murray's talks on MCMC were my personal favorites on the program.

Contact

Peter Orbanz
Computational and Biological Learning Laboratory
University of Cambridge
Email: p.orbanz@eng.cam.ac.uk
Phone: +44-1223-748518
Postal Address: Room BE441, Baker Building, Trumpington Street, Cambridge CB2 1PZ, UK