Welcome to the CLRC

The Computer Learning Research Centre is a world-class research centre dedicated to the development of machine learning theory.

Our objectives are to:

• Develop machine learning theory


• Design and implement software based on the algorithms and theory developed by the Centre


• Provide consultancy, advice and seminars to external organizations


• Serve as a springboard for high-quality interdisciplinary research

Upcoming events:

• Professor Robert Merton (winner of the 1997 Nobel Prize in Economics) will present the 2010 Kolmogorov Lecture entitled "Observations on the Science of Finance in the Practice of Finance: Past, Present and Future" at 6pm on 23 February, Windsor Building Auditorium (The abstract of the talk can be found here).


• The 2010 Workshop on Game-Theoretic Probability and Related Topics will be held at CLRC on 21 - 23 June.

Recent events:

• Sad news: on 7 December 2009 Ray J. Solomonoff passed away.


• On 8 October 2009, Glenn Shafer and his colleague Arthur Dempster were made honorary doctors in economics by the University of Economics, Prague. See Professor Shafer's acceptance speech.


• Yuri Gurevich of Microsoft Research gave a talk on "The Church-Turing Thesis: Story and Recent Progress" on 24th March 2009.


• Congratulations to our PhD student Misha Dashevskiy for winning the E.M.Gold award and a prize for the best paper in the 19th International conference on Algorithmic Learning Theory (ALT2008). The title of the paper is "Aggregating Algorithm for a Space of Analytic Functions". The abstract of the paper is here.
  
  
• A new technique for "hedging" predictions was presented and discussed recently by Alexander Gammerman and Vladimir Vovk at a special meeting of the British Computer Society. The method can be applied to many algorithms, including Support Vector Machines, Kernel Ridge Regression, Kernel Nearest Neighbours and other state-of-the-art methods.
  
  The hedged predictions include confidence measures that are provably valid, and it becomes possible to control the number of errors by selecting a suitable confidence level. The discussants of the technique included Vladimir Vapnik, Alexey Chervonenkis, Glenn Shafer, Zhiyuan Luo and many others. The paper and the discussion can be found here.
  
  
• Volodya Vovk, Alex Gammerman and Yura Kalnishkan were awarded a three-year grant from the ESPRC entitled Practical Competitive Prediction (see "web-site").
  
  
• Alex Gammerman, Volodya Vovk and Zhiyuan Luo were awarded a new grant from the Veterinary Laboratories Agency (VLA). It is entitled "Development and Application of Machine Learning Algorithms for the Analysis of Complex Veterinary Data Sets".
  
  
• Alex Gammerman and Volodya Vovk were awarded a new grant from the Cyprus Government. It is entitled "Development of New Conformal Prediction Methods with Applications in Medical Diagnosis."
  
  
• A new webpage was created for the Kolmogorov Lecture and Medal. The 2007 Kolmogorov Lecture was presented by Professor Yakov Sinai on 29th January 2007. His talk was entitled "Renormalization Group Theory in Probability and Analysis."
  
  
• New pages for the MRC-funded project, "Proteomic Analysis of the Human Serum Proteome for Population Screening, Diagnosis and Biomarker Discovery" were added to the CLRC Projects page.
  
  
• Dr Chris Watkins, along with fellow authors Dr Alex Clark and Dr Christophe Costa Florencio, won the Best Paper Award at the European Conference on Machine Learning (ECML) for their paper entitled "Languages as Hyperplanes: Grammatical Inference with String Kernels."
  
  
• CLRC members Tony Bellotti, Zhiyuan Luo and Alex Gammerman received the "Best Paper Award" at the 2006 IEEE International Conference on Granular Computing for their paper entitled "Reliable Classification of Childhood Acute Leukaemia from Gene Expression Data using Confidences Machines."
  
  
• On the 10th of February 2006 the National Academy of Engineering (NAE) of USA elected Professor Vladimir Vapnik of Computer Learning Research Centre (CLRC) and the Computer Science Department as a new member for "insights into the fundamental complexities of learning and for inventing practical and widely applied machine-learning algorithms".
  
  
• Professors Vovk, Gammerman and Shafer published a monograph on conformal prediction titled Algorithmic Learning in a Random World