Information Theory in Machine Learning and Data Science

Dear Colleagues,

The field of machine learning and data science is concerned with the design and analysis of algorithms for making decisions, reasoning about the world, and knowledge extraction from massive amounts of data. On the one hand, the performance of machine learning algorithms is limited by the amount of predictively relevant information contained in the data. On the other hand, different procedures for accessing and processing data can be more or less informative. Claude Shannon originally developed information theory with communication systems in mind. However, it has also proved to be an indispensible analytical tool in the field of mathematical statistics, where it is used to quantify the fundamental limits on the performance of statistical decision procedures and to guide the processes of feature selection and experimental design.

The purpose of this Special Issue is to highlight the state-of-the-art in applications of information theory to the fields of machine learning and data science. Possible topics include, but are not limited to, the following:

• Fundamental information-theoretic limits of machine learning algorithms

• Information-directed sampling and optimization

• Statistical estimation, optimization, and learning under information constraints

• Information bottleneck methods

• Information-theoretic approaches to adaptive data analysis

• Information-theoretic approaches to feature design and selection

• Estimation of information-theoretic functionals

Prof. Dr. Maxim Raginsky
Guest Editor

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