News
- I plan to graduate in the summer/fall of 2012 and will
be on the job market sometime afterwards. Please contact me
if you think you know of a position that might interest me.
Here you can find an up-to-date CV.
- Paper accepted to ICML 2012.
- Two papers accepted to AISTATS 2012.
About
I'm a machine learning PhD student in the group
of Prof. Joachim M. Buhmann at ETH Zurich, Switzerland.
Research
My main research interest is centered around structured
output prediction and learning. I'm trying to come up with novel approaches for
learning such models, which hopefully lead to more efficient approximate
training. I'm also interested in the very related topic of
inference in graphical models.
Publications
I also try to keep my Google
scholar profile up to date.
Peer Reviewed Conferences and Journals
Patrick Pletscher & Sharon Wulff
LPQP for MAP: Putting LP Solvers to Better Use
ICML, 2012. To appear.
Patrick Pletscher & Pushmeet Kohli
Learning Low-order Models for Enforcing High-order Statistics
AISTATS, 2012.
[
PDF]
[
Publisher]
[
poster]
[
BibTeX]
@inproceedings{Pletscher2012b,
author = {Pletscher, Patrick and Kohli, Pushmeet},
title = {Learning Low-order Models for Enforcing High-order Statistics},
booktitle = {Proceedings of the Fifteenth International
Conference on Artificial Intelligence
and Statistics ({AISTATS}) 2012},
pages = {886--894},
year = {2012},
publisher = {JMLR: W\&CP 22},
address = {La Palma, Canary Islands},
editor = {Neil Lawrence and Mark Girolami}
}
Patrick Pletscher & Cheng Soon Ong
Part & Clamp: Efficient Structured Output Learning
AISTATS, 2012.
[
PDF]
[
Publisher]
[
poster]
[
BibTeX]
@inproceedings{Pletscher2012a,
author = {Pletscher, Patrick and Ong, Cheng Soon},
title = {Part & Clamp: Efficient Structured Output Learning},
booktitle = {Proceedings of the Fifteenth International
Conference on Artificial Intelligence
and Statistics ({AISTATS}) 2012},
pages = {877--885},
year = {2012},
publisher = {JMLR: W\&CP 22},
address = {La Palma, Canary Islands},
editor = {Neil Lawrence and Mark Girolami}
}
Patrick Pletscher, Sebastian Nowozin, Pushmeet Kohli & Carsten
Rother
Putting MAP back on the Map
33rd Annual Symposium of the German Association for Pattern Recognition (DAGM), 2011.
[
PDF]
[
Publisher]
[
supplement]
[
poster]
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BibTeX]
@inproceedings{Pletscher2011,
author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Rother, Carsten},
title = {Putting {MAP} back on the Map},
booktitle = {33rd Annual Symposium of the German Association for Pattern Recognition ({DAGM})},
year = {2011},
editor = {R. Mester and M. Felsberg},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {6835},
pages = {111--121},
}
Patrick Pletscher, Cheng Soon Ong & Joachim M. Buhmann
Entropy and Margin Maximization for Structured Output Learning
Proceedings of the 20th European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2010.
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PDF]
[
Publisher]
[
supplement]
[
talk]
[
poster]
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BibTeX]
@inproceedings{Pletscher2010,
author = {Pletscher, Patrick and Ong, Cheng Soon and Buhmann, Joachim M.},
title = {Entropy and Margin Maximization for Structured Output Learning},
booktitle = {Proceedings of the 20th European Conference on Machine Learning ({ECML})},
year = {2010},
editor = {Jos{\'e} L. Balc{\'a}zar and
Francesco Bonchi and
Aristides Gionis and
Mich{\`e}le Sebag},
series = {Lecture Notes in Computer Science},
volume = {6321},
pages = {83-98},
}
Patrick Pletscher, Cheng Soon Ong & Joachim M. Buhmann
Spanning Tree Approximations for Conditional Random Fields
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics
(AISTATS), 2009.
[
PDF]
[
Publisher]
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BibTeX]
@inproceedings{Pletscher2009,
author = {Pletscher, Patrick and Ong, Cheng Soon and Buhmann, Joachim M.},
title = {Spanning Tree Approximations for Conditional Random Fields},
booktitle = {Proceedings of the Twelfth International Conference on Artificial
Intelligence and Statistics ({AISTATS}) 2009},
year = {2009},
pages = {408--415},
editor = {D. van Dyk and M. Welling},
publisher = {JMLR: W&CP 5},
address = {Clearwater Beach, Florida}
}
Matthew Brand & Patrick Pletscher
A conditional random field for automatic photo editing
Proceedings of the IEEE Conferencer on Computer Vision and Pattern Recognition (CVPR), 2008.
[
PDF]
[
Publisher]
[
BibTeX]
@inproceedings{Brand2008,
author = {Matthew Brand and Patrick Pletscher},
title = {A conditional random field for automatic photo editing},
booktitle = {Proceedings of the {IEEE} Conference on Computer
Vision and Pattern Recognition ({CVPR})},
year = {2008},
}
Isabelle Guyon, Jiwen Li, Theodor Mader, Patrick Pletscher, Georg Schneider & Markus Uhr
Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark
Pattern Recognition Letters, Volume 28, Issue 12, September 2007, Pages 1438-1444.
[
PDF]
[
Publisher]
[
BibTeX]
@article{Guyon2007,
author = {Isabelle Guyon and Jiwen Li and Theodor Mader and
Patrick A. Pletscher and Georg Schneider and Markus Uhr},
title = {Competitive baseline methods set new standards for the
{NIPS} 2003 feature selection benchmark},
journal = {Pattern Recognition Letters},
volume = {28},
number = {12},
year = {2007},
issn = {0167-8655},
pages = {1438--1444},
doi = {http://dx.doi.org/10.1016/j.patrec.2007.02.014},
publisher = {Elsevier Science Inc.},
address = {New York, NY, USA},
}
Workshops
Patrick Pletscher & Sharon Wulff
A Combined LP and QP Relaxation for MAP
NIPS Workshop on Discrete Optimization in Machine Learning
(DISCML), 2011.
[
PDF]
[
spotlight]
[
poster]
[
BibTeX]
@inproceedings{Pletscher2011a,
author = {Pletscher, Patrick and Wulff, Sharon},
title = {A Combined LP and QP Relaxation for MAP},
booktitle = {NIPS Workshop on Discrete Optimization in Machine Learning (DISCML)},
year = {2011}
}
Reports
Patrick Pletscher
Model order selection: Criteria, inference strategies and an application to biclustering
Master's thesis, ETH Zurich, September 2007.
Supervised by Peter Orbanz and Prof. Buhmann.
[
PDF]
[
BibTeX]
@mastersthesis{Pletscher2007,
author = {Patrick Pletscher},
title = {{Model order selection: Criteria, inference strategies and an application to biclustering}},
school = {ETH Zurich},
address = {Switzerland},
year = {2007},
month = {September},
}
Peptide Assignment Validation
Semester thesis in machine learning, 2006.
Supervised by Bernd Fischer and Prof. Buhmann.
[
PDF]
[
talk]
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BibTeX]
@misc{Pletscher2006,
author = {Patrick Pletscher},
title = {{Peptide Assignment Validation: Telling what's wrong without
actually knowing what's right}},
school = {ETH Zurich},
address = {Switzerland},
year = {2006},
month = {April},
note = {semester project report},
}
Adaptive Security of Compositions
Semester thesis in cryptography, 2005.
Supervised by Krzysztof Pietrzak and Prof. Maurer.
[
PDF]
[
talk]
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BibTeX]
@misc{Pletscher2005,
author = {Patrick Pletscher},
title = {{Adaptive security of compositions}},
school = {ETH Zurich},
address = {Switzerland},
year = {2005},
month = {July},
note = {semester project report},
}
Software
Graph cut MEX wrapper
Matlab MEX wrapper for Vladimir Kolmogorov's graph cut code.
graphcut-1.0.tar.gz (Release date: 2010/03/17).
Nonparametric Bayesian Biclustering
Nonparametric Baysian Biclustering with a Double Mixture Model. This work was carried out in parts during my master's thesis.
npbb-1.0.tar.gz (Release date: 2008/12/26).
Teaching
I have assisted several courses at ETH Zurich as a teaching assistant.
- Computational Intelligence Lab SS12
- Probabilistic Graphical Models for Image Analysis WS11
- Computational Intelligence Lab SS11
- Probabilistic Graphical Models for Image Analysis WS10
- Computational Intelligence Lab SS10
- Probabilistic Graphical Models for Image Analysis WS09
- Informatik II (D-MAVT) SS09
- Image Analysis with Statistical Models WS08
- Visual Computing SS08
- Computational Science SS07
- Computational Science SS06
- Informatik I (D-BAUG) WS05/06
If you are an undergraduate student at ETH Zurich and looking for a
research topic for e.g. a master's thesis, then feel free to contact me.
Possible topics include approximate inference and learning in graphical
models and its applications to e.g. computer vision.
