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.

Research

My main research interest are the statistics of discrete objects and structures: permutations, graphs, partitions, and even good, old-fashioned sequences of ones and zeros. From a methodological point of view, much of my work is concerned with Bayesian nonparametric models.

Working Papers

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

Projective Limit Techniques in Bayesian Nonparametrics.
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, 2009. Technical Report. [PDF (arXiv)]

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

PhD Thesis

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

Odds & Ends

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.

Nonparametric Bayes Tutorial

A series of three tutorial talks on the theoretical foundations of nonparametric Bayesian methods. My tutorial webpage contains the talk slides, as well as an annotated list of references on Bayesian nonparametrics and related topics.

Teaching Material: Machine Learning

The teaching material (slides and exercise sheets) I drew up as a teaching assistant at ETH Zurich is available here: Machine Learning Slides & Exercises Problems Judging from the surprising number of downloads, my exercise problems seem to be more popular than some of my papers. I also supervised the Student Theses of some really excellent students at ETH.

Contact

Peter Orbanz
Computational and Biological Learning Laboratory
University of Cambridge
Email: p.orbanz@eng.cam.ac.uk
Phone: +44-1223-748518
Postal Address:

Dr Peter Orbanz | University of Cambridge
Room BE441, Baker Building | Trumpington Street | Cambridge CB2 1PZ, UK