Event: Conferences » Other » 26th Annual Conference on Learning Theory (COLT), Princeton 2013 26th Annual Conference on Learning Theory (COLT), Princeton 2013

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COLT 2013 - Princeton   

26th Annual Conference on Learning Theory (COLT), Princeton 2013

The conference is a single track meeting that includes invited talks as well as oral presentations of all refereed papers. We invited submissions of papers addressing theoretical aspects of machine learning and related topics. We strongly support a broad definition of learning theory, including, but not limited to:

  1. Design and analysis of learning algorithms and their generalization ability
  2. Computational complexity of learning
  3. Optimization procedures for learning
  4. Unsupervised, semi-supervised learning and clustering
  5. Online learning
  6. Active learning
  7. High dimensional and non-parametric empirical inference, including sparsity methods
  8. Planning and control, including reinforcement learning
  9. Learning with additional constraints: E.g. privacy, time or memory budget, communication
  10. Learning in other settings: E.g. social, economic, and game-theoretic
  11. Analysis of learning in related fields: natural language processing, neuroscience, bioinformatics, privacy and security, machine vision, data mining, information retrieval.

For more information visit the COLT 2013 website.


Invited Talks

Online Learning (I)

Online Learning (II)

Computational Learning Theory (I)

Computational Learning Theory (II)

Computational Learning Theory (III)

Unsupervised Learning

Dimensionality Reduction and Loss Function

Statistical Learning Theory (I)

Statistical Learning Theory (II)

Active Learning


Reviews and comments:

Comment1 Almira Kaye, December 2, 2021 at 1:21 p.m.:

This is a great discussion.

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