COLT 2009

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.)

---