COLT 2009
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See: COLT Conference, 2009, COLT 2010, COLT 2008.
References
- http://www.cs.mcgill.ca/~colt2009/cfp.html
- We strongly support a broad definition of learning theory, including:
- Analysis of learning algorithms and their generalization ability
- Computational complexity of learning
- Bayesian analysis
- Statistical mechanics of learning systems
- Optimization procedures for learning.
- Kernel Methods.
- Inductive inference.
- Boolean function learning.
- Unsupervised and semi-supervised learning and clustering.
- On-line learning and relative loss bounds.
- Learning in planning and control (including reinforcement learning)
- Mathematical analysis of learning in related fields (e.g. game theory, natural language processing, neuroscience, bioinformatics, privacy and security, machine vision, data mining, information retrieval, etc.)
- We strongly support a broad definition of learning theory, including: