Peter Orbanz
Welcome.
I'm a Research Associate (a postdoc) in the Machine Learning
Group of
Zoubin Ghahramani
at the University of Cambridge.
I hold a degree in computer science and mathematics from the University of
Bonn, and obtained my PhD in the
Pattern Analysis and Machine Learning Group
at ETH Zurich, where I worked with
Joachim M. Buhmann.
Contact
|
Peter Orbanz
Computational and Biological Learning Laboratory
[link]
University of Cambridge
|
|
Research
I am interested in all mathematical aspects of pattern recognition
and artifical intelligence. My current work focusses on the
structural properties of Bayesian models, both in the parametric and
nonparametric case.
Working Papers
-
Conjugate Projective Limits.
P Orbanz. Preprint.
-
Functional Conjugacy in Parametric Bayesian Models.
P Orbanz, 2009. Preprint.
[PDF]
-
Construction of Nonparametric Bayesian Models from
Parametric Bayes Equations.
P Orbanz, 2009. Preprint.
[PDF]
Publications
-
Bayesian Nonparametric Models.
P Orbanz and YW Teh. In Encyclopedia of Machine
Learning (Springer), to appear.
[PDF]
-
Construction of Nonparametric Bayesian Models from
Parametric Bayes Equations.
P Orbanz. NIPS 2009.
[PDF]
[Supplements (Proofs)]
-
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. In 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.
In 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. In EMMCVPR, 2007.
[PDF]
[Publisher]
-
Smooth Image Segmentation by Nonparametric
Bayesian Inference.
P Orbanz and JM Buhmann. In European Conference on Computer
Vision (ECCV), Vol. 1, 444-457, 2006.
[Publisher]
-
SAR Images as Mixtures of Gaussian Mixtures.
P Orbanz and JM Buhmann. In IEEE International Conference on Image
Processing (ICIP), Vol. 2, 209-212, 2005.
[PDF]
[Publisher]
PhD Thesis
-
Infinite-Dimensional Exponential Families in the Cluster
Analysis of Structured Data
ETH Zurich, 2008.
[PDF]
Tutorials, Teaching, etc
I have no teaching obligations at Cambridge, but I try to make up
for it with tutorials and other ways to force myself upon the general
public. I also keep some older teaching material available online.
Machine Learning Summer School 2009
We are organizing this year's edition of the Machine Learning
Summer School here in Cambridge.
[Website.]
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
Judging from the surprising number of downloads, my exercise
problems seem to be more popular than some of my papers.
Student's Theses
Theses I supervised at ETH Zurich.
-
Patrick Pletscher:
Model Order Selection: Criteria,
Inference Strategies, and an Application to Biclustering.
Master Thesis, ETH Zurich, 2007. [PDF]
-
Ludwig Busse:
Clustering Rank Data.
Research Project, ETH Zurich, 2007.
-
Samuel Braendle:
Feature Extraction for Bayesian
Order-Adaptive Clustering.
Term Thesis, ETH Zurich, 2007.
[PDF]
-
Sarah Gugl:
Classification of Radar Data Based on
Constrained Agglomerative Segmentation.
Term Thesis, ETH Zurich, 2006.