Research

I am interested in various aspects of Machine Learning and Bayesian inference. It includes deterministic approximate methods and stochastic methods. Here you'll find preprints, papers, talks and notes.

Publications

A penny for your thoughts? The value of information in recommendation systems (pdf)
Alexandre Passos, Jurgen Van Gael, Ralf Herbrich, and Ulrich Paquet.
NIPS 2011 Workshop on Bayesian Optimization, Experimental Design, and Bandits, Sierra Nevada, Spain, 2011.

Cumulant expansions for improved inference with EP in discrete Bayesian networks (pdf)
Manfred Opper, Ulrich Paquet, and Ole Winther.
3rd NIPS Workshop on Discrete Optimization in Machine Learning, Sierra Nevada, Spain, 2011.

A Hierarchical Model for Ordinal Matrix Factorization (preprint pdf; final publication available at www.springerlink.com)
Ulrich Paquet, Blaise Thomson, and Ole Winther.
Statistics and Computing, Volume 21(3), 2011.

Vuvuzelas & Active Learning for Online Classification (pdf)
Ulrich Paquet, Jurgen van Gael, David Stern, Gjergji Kasneci, Ralf Herbrich, and Thore Graepel.
Computational Social Science and the Wisdom of Crowds Workshop (colocated with NIPS 2010), 2010.

Large-scale Ordinal Collaborative Filtering (pdf)
Ulrich Paquet, Blaise Thomson, and Ole Winther.
1st Workshop on Mining the Future Internet, Future Internet Symposium, Berlin, September 2010.

Perturbation Corrections in Approximate Inference: Mixture Modelling Applications (pdf)
Ulrich Paquet, Manfred Opper, and Ole Winther.
Journal of Machine Learning Research, Volume 10, 935-976, 2009.

Convexity and Bayesian Constrained Local Models (pdf)
Ulrich Paquet.
CVPR (Computer Vision and Pattern Recognition) 2009. This paper contains ideas useful for face recognition. A summary poster is also available.

Improving on Expectation Propagation (pdf)
Manfred Opper, Ulrich Paquet, and Ole Winther.
Advances in Neural Information Processing Systems 21, 2009.

Gaussian Process Modeling for Image Distortion Correction in EPI
Joseph W. Stevick, Sally G. Harding, Ulrich Paquet, Richard E. Ansorge, T. Adrian Carpenter, and Guy B. Williams.
Magnetic Resonance in Medicine, Volume 59(3), 598-606, 2008.

Bayesian Inference for Latent Variable Models (pdf)
Ulrich Paquet.
PhD Thesis. Computer Laboratory, University of Cambridge, 2007.

Gaussian Process Modeling for EPI Distortion Correction (pdf)
Joseph .W. Stevick, Sally G. Harding, Ulrich Paquet, Richard E. Ansorge, T. Adrian Carpenter, and Guy B. Williams.
Joint Annual Meeting ISMRM-ESMRMB, 2007.

Bayesian Hierarchical Ordinal Regression (ps)
Ulrich Paquet, Sean Holden, and Andrew Naish-Guzman.
Proceedings of the International Conference on Artificial Neural Networks, 2005.

On The Explicit Use Of Example Weights In The Construction Of Classifiers (ps)
Andrew Naish-Guzman, Sean Holden, and Ulrich Paquet.
Proceedings of the International Conference on Artificial Neural Networks, 2005.

I did research for my Master's degree under prof. Andries Engelbrecht at the University of Pretoria, South Africa. The following publications, as well as my thesis (Training Support Vector Machines with Particle Swarms), can be downloaded from the Computational Intelligence Research Group site.

Particle Swarms for Linearly Constrained Optimisation
Ulrich Paquet and Andries P. Engelbrecht.
Fundamenta Informaticae, 76(1-2), 147-170, 2007.

A New Particle Swarm Optimiser for Linearly Constrained Optimisation
Ulrich Paquet and Andries P. Engelbrecht.
Proceedings of the Congress on Evolutionary Computation, pages 227-233, 2003.

Training Support Vector Machines with Particle Swarms
Ulrich Paquet and Andries P. Engelbrecht.
Proceedings of the International Joint Conference on Neural Networks, 2003.

Talks and various notes

Video Large-scale Bayesian Inference for Collaborative Filtering (video, slides)
Ole Winther, with Ulrich Paquet, and Blaise Thomson.
NIPS Workshop on Approximate Bayesian Inference in Continuous/Hybrid Models, 2007.

Video Improving on Expectation Propagation (video, slides, poster)
Ulrich Paquet, with Ole Winther and Manfred Opper.
NIPS Workshop on Approximate Bayesian Inference in Continuous/Hybrid Models, 2007.

Video Perturbative Corrections to Expectation Consistent Approximate Inference (video, slides)
Manfred Opper, with Ulrich Paquet and Ole Winther.
NIPS Workshop on Approximate Bayesian Inference in Continuous/Hybrid Models, 2007.

Empirical Bayesian Change Point Detection (pdf)
Ulrich Paquet.
2007.