The 27th Annual Conference on Learning Theory (COLT 2014) will take place in Barcelona, Spain, on June 13-15, 2014.
Click here to read the complete call for papers.
We invite submissions of papers addressing theoretical aspects of machine learning and related topics. Submissions by authors who are new to COLT are encouraged. 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
• Interactive learning
• Kernel Methods
• 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.
We are also interested in papers that include viewpoints that are new to the COLT community. We welcome experimental and algorithmic papers provided they are relevant to the focus of the conference by elucidating theoretical results. Also, while the primary focus of the conference is theoretical, papers can be strengthened by the inclusion of relevant experimental results.