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:
- Design and analysis of learning algorithms and their generalization ability
- Computational complexity of learning
- Optimization procedures for learning
- Unsupervised, semi-supervised learning and clustering
- Online learning
- Active learning
- High dimensional and non-parametric empirical inference, including sparsity methods
- Planning and control, including reinforcement learning
- Learning with additional constraints: E.g. privacy, time or memory budget, communication
- Learning in other settings: E.g. social, economic, and game-theoretic
- 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.
Online Learning (I)
Online Learning (II)
Computational Learning Theory (I)
Computational Learning Theory (II)
Computational Learning Theory (III)
Dimensionality Reduction and Loss Function
Statistical Learning Theory (I)
Statistical Learning Theory (II)
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