Large Scale Bayesian Experimental Design: Adaptive Compressive Sensing in the Real World
Workshop on Adaptive Sensing, Active Learning, and Experimental Design.
Neural Information Processing Systems 22 (December 2009).
[pdf]
Bayesian Sampling Optimization for Magnetic Resonance Imaging
IW-SMI2009 Workshop Statistical Mechanics, Kyoto, Japan (September 2009).
[pdf]
Bayesian Trajectory Optimization for Magnetic Resonance Imaging Sequences
Compressive Sensing Workshop, Duke University, Durham NC (February 2009).
[pdf, video]
Bayesian Optimization of MRI Sequences
Bernstein Focus Workshop, FIAS, Frankfurt (November 2008).
Compared to earlier talks, this has a few more details about the inference
algorithm.
[pdf]
Information Consistency of Nonparametric Gaussian Process Methods
Minimum Description Length Workshop, ICML 2008.
[pdf, video]
Large Scale Approximate Inference and Experimental Design for Sparse Linear Models
Isaac Newton Institute, Cambridge, UK (June 2008).
[pdf, video]
Expectation Propagation and Experimental Design for the Sparse Linear Model
Isaac Newton Institute, Cambridge, UK (February 2008).
[pdf]
Gaussian Processes For Machine Learning: Where Are We, And Where Could We Go?
Workshop on Gaussian Processes, NIPS (December 2005).
[pdf]
An Overview of Semi-Supervised Learning
Workshop on Semi-supervised Learning, ICML (August 2005).
[PowerPoint]
PAC-Bayesian Theorems for Bayesian Gaussian Process Classification
NeuroCOLT Workshop, Windsor, London (April 2002).
NCRG Seminar, Aston University, Birmingham (February 2002).
[ps.gz]
Optimization of k-Space Trajectories for Compressive Sensing by Bayesian Experimental Design
Biomedical Magnetic Resonance Seminar, ETH Zuerich (October 2009).
Specifically for an MRI audience. Detail slides at the end give a brief intro
to the algorithm.
[pdf]
Approximate Bayesian Inference and Measurement Design Optimization for Low-Level Computer Vision and Imaging
Max Planck Institute for Informatics, Saarbruecken (March 2009).
Watch this for some more details about the double loop algorithm.
[pdf]
Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design
University Hospital Mannheim (March 2009).
[pdf]
Bayesian Optimization of MRI Sequences
MMCI Kickoff Workshop, Saarland University, Saarbruecken (November 2008).
[pdf]
Bayesian Experimental Design of MRI Sequences
Rapid MRI: Beyond the Nyquist limit, Freiburg (Oktober 2008).
Our first talk for MRI audiences. Since then, we improved our method and obtain
good results for Cartesian undersampling as well.
[pdf]
Compressed Sensing and Bayesian Experimental Design
International Conference on Machine Learning 25 (2008).
Jointly with H. Nickisch. Watch this talk if you still believe that randomly
drawn projections are a good idea for measuring real-world images.
[pdf, video]
Large Scale Approximate Inference for Bayesian Image Reconstruction
and Measurement Design
GIF Workshop, Tuebingen (May 2008).
A first glimpse on the large scale double loop algorithm.
[pdf]
Experimental Design for Efficient Identification of Gene Regulatory Networks using Sparse Bayesian Models
Workshop on Probabilistic Modelling in Computational Biology, Vienna (July 2007).
[pdf]
Approximate Bayesian Inference for Sparse Generalized Linear Models
Machine Learning Seminar, UCL, London (June 2007).
[pdf]
Experimental Design for Efficient Identification of Gene Regulatory Networks using Sparse Bayesian Models
Workshop on Parameter Estimation in Systems Biology, Manchester (March 2007).
The Vienna talk above is less technical, but about the same work.
[pdf, video]
Sparse Gaussian Process Classification With Multiple Classes
Snowbird Learning Workshop (April 2004).
[PowerPoint]
Sparse Approximations to Gaussian Processes
Workshop on AI and Statistics 9 (January 2003).
[PowerPoint]
PAC-Bayesian Theorems for Gaussian Process Classification
Gatsby Neuroscience Unit, London (October 2002).
[PowerPoint]
Sparse Approximations to Bayesian Gaussian Processes
Gatsby Neuroscience Unit, London (October 2002).
Gaussian Process Workshop, Edinburgh (September 2002).
[PowerPoint]
Introduction to Gaussian Processes
ANC Workshop, Edinburgh (February 2002).
[ps.gz]
Approximation Techniques for Gaussian Process Methods
ANC Seminar, Edinburgh (September 2001).
[ps.gz]
Learning with Labeled and Unlabeled Data
First-year Talk, Edinburgh (February 2001).
[ps.gz]
Variational Bayesian Model Selection for Support Vector Classifiers
ICANN Kernel Workshop, Edinburgh (September 1999).
[ps.gz]
Applications of Information Theory, or the Narrow Edge between Ruin and Infinite Wealth
ANC Seminar, Edinburgh (June 1999).
[ps.gz]
Probabilistic Interpretations of Support Vector Machines and Other Spline Smoothing Models
Kernel Workshop EuroCOLT, Nordkirchen, Germany (March 1999).
[ps.gz]
Bayesian Methods for Gaussian Processes
Seminar Statistische Lerntheorie, Karlsruhe, Germany (November 1998).
[ps.gz]