Special Issue "The Information Bottleneck Method"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (1 February 2013).
Prof. Dr. Naftali Tishby
1. School of Engineering and Computer Science 2. Director, Interdisciplinary Center for Neural Computation3. Flinkman Professor of Brain Research, Edmond and Lilly Safra Center for Brain Sciences, The Hebrew University, Jerusalem 91904, Israel
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Interests: information theory in machine learning, dynamical systems and control, statistical physics of neural systems, computational neuroscience
The Information Bottleneck Method is a simple optimization principle for a model-free extraction the relevant part of one random variable with respect to another. Its formulation is closely related to classical problems in information theory, such as Rate-Distortion Theory and channel coding with side-information (the WAK problem). But it turned out to be connected with many other interesting domains. It generalizes the notion of minimal sufficient statistics in classical estimation theory; generalizes the Canonical-Correlation-Analysis (CCA) when applied to multivariate Gaussian variables; provides an optimal solution to the Kelly gambling problem; and serves as a basic building block for an information theory of perception and action. It provides elegant extensions of optimal control theory to information gathering problems, and has numerous applications in machine learning and computational neuroscience. This special issue should provide a thorough discussion of the statistical, algorithmic, control theoretic, and biological aspects of this suggestive principle.
Prof. Dr. Naftali Tishby
Dr. Daniel Polani
Manuscript Submission Information
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- information divergences
- sufficient statistics
- predictive information
- information and control
- side information
- cognitive information processing