Congratulations to Andrew Gordon Wilson for winning the £10,000 G-Research prize for his PhD dissertation, “Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian Processes”!

The models introduced in Andrew’s dissertation follow several high level principles:
1) We can typically improve the predictive performance of a model by accounting for additional structure in data.
2) To develop truly intelligent systems — statistical models which can automatically discover patterns in data, perform extrapolation, and learn and make decisions without human intervention — we should develop highly expressive models with the appropriate inductive biases.
3) We most need expressive models for large datasets, which typically provide more information for learning structure.
4) We can often exploit the existing inductive biases (assumptions) or structure of a model for scalable inference, without the need for simplifying assumptions.

Underlying many popular models in machine learning is a ‘covariance kernel’. The covariance kernel controls the expressive power and inductive biases of such models. Knowing which kernel to use is difficult, and is generally an unresolved problem. Sometimes expert statisticians hand craft kernels for specialized applications. However, most popular kernels can only be used for smoothing and interpolation. Andrew’s thesis introduces new covariance kernels which enable pattern discovery and extrapolation without human intervention — a step towards automating statistics, and solving kernel selection problems. Moreover, Andrew’s work exploits the structure of these kernels so that these models can scale to massive datasets, with no loss in predictive accuracy. The models in his thesis enable new applications and state of the art results in econometrics, geostatistics, nuclear magnetic resonance spectroscopy, time series modelling, kernel discovery, acoustic modelling, image inpainting, texture extrapolation, and video analysis.

Andrew is soon starting a research fellowship in the SAILING machine learning group at Carnegie Mellon University.

More information about Andrew’s award can be found here:

• G-Research PhD Prize Information
• PhD Prize Call for entries

Carl Rasmussen’s project on getting a unicycle to stand on its own using techniques from Bayesian reinforcement learning is discussed by Acorn founder Herman Hauser in an interview with BBC Business.  Herman Hauser discusses the interactive technologies that will be “big in 2014″, of which he says machine learning will play a crucial part.

Watch the interview here.

Prof. Zoubin Ghahramani speaks to BBC Radio 4 on a programme about ‘Deep Learning’.

Listen to the broadcast here.