24th Annual Conference on Learning Theory, Budapest, Hungary
9 July 2011 - 9 July 2011
Hungary
The 24th Annual Conference on Learning Theory will take place from 9 to 11 July 2011 in Budapest, Hungary.
The field of learning theory looks at the process of how humans learn, and has two principle objectives. One is in providing a vocabulary and conceptual framework for interpreting the examples of learning that may be observed. The other is in suggesting where to look for solutions to practical problems.
While learning theory can be classified under one of several different philosophical frameworks, the event will focus on experimental and algorithmic research, as well as related experimental results. Specific topics are set to include:
- analysis of learning algorithms and their generalisation ability;
- computational complexity of learning;
- Bayesian analysis;
- statistical mechanics of learning systems;
- optimisation procedures for learning;
- kernel methods;
- Boolean function learning;
- unsupervised and semi-supervised learning and clustering;
- online learning and relative loss bounds;
- planning and control, including reinforcement learning;
- learning in social, economic, and game-theoretic settings;
- analysis of learning in related fields: natural language processing, neuroscience, bioinformatics, privacy and security, machine vision, data mining, information retrieval.
The event will be co-located with the Foundations of Computational Mathematics conference.For further information, please visit: http://colt2011.sztaki.hu/index.html(opens in new window)
The field of learning theory looks at the process of how humans learn, and has two principle objectives. One is in providing a vocabulary and conceptual framework for interpreting the examples of learning that may be observed. The other is in suggesting where to look for solutions to practical problems.
While learning theory can be classified under one of several different philosophical frameworks, the event will focus on experimental and algorithmic research, as well as related experimental results. Specific topics are set to include:
- analysis of learning algorithms and their generalisation ability;
- computational complexity of learning;
- Bayesian analysis;
- statistical mechanics of learning systems;
- optimisation procedures for learning;
- kernel methods;
- Boolean function learning;
- unsupervised and semi-supervised learning and clustering;
- online learning and relative loss bounds;
- planning and control, including reinforcement learning;
- learning in social, economic, and game-theoretic settings;
- analysis of learning in related fields: natural language processing, neuroscience, bioinformatics, privacy and security, machine vision, data mining, information retrieval.
The event will be co-located with the Foundations of Computational Mathematics conference.For further information, please visit: http://colt2011.sztaki.hu/index.html(opens in new window)