Advances in minimum description length: Theory and applications

Advances in minimum description length: Theory and applications pdf

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Peter Grünwald

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English

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Peter Grünwald heads the machine learning group at CWI in Amsterdam, the Netherlands. He is also full professor of Statistical Learning at the mathematical institute of Leiden University. Currently the President of the Association for Computational Learning, the organization running COLT, the world’s prime annual conference on machine learning theory, he was co-program chair of COLT in 2015 and also chaired UAI – another top ML conference – in 2010/2011. Apart from publishing at ML venues like NIPS, COLT and UAI, he also regularly contributes to statistics journals such as the Annals of Statistics. He is the author of the book The Minimum Description Length Principle, (MIT Press, 2007; see here for an up-to-date (2020), much shorter introduction), which has become the standard reference for the MDL approach to learning. In 2010 he was co-awarded the Van Dantzig prize, the highest Dutch award in statistics and operations research. He received NWO VIDI (2005), VICI (2010) and TOP-1 (2016) grants.

Book Description

Advances in minimum description length: Theory and applications pdf by Peter Grünwald

The process of inductive inference—to infer general laws and principles from particular instances—is the basis of statistical modeling, pattern recognition, and machine learning. The Minimum Descriptive Length (MDL) principle, a powerful method of inductive inference, holds that the best explanation, given a limited set of observed data, is the one that permits the greatest compression of the data—that the more we are able to compress the data, the more we learn about the regularities underlying the data. Advances in Minimum Description Length is a sourcebook that will introduce the scientific community to the foundations of MDL, recent theoretical advances, and practical applications. The book begins with an extensive tutorial on MDL, covering its theoretical underpinnings, practical implications as well as its various interpretations, and its underlying philosophy. The tutorial includes a brief history of MDL—from its roots in the notion of Kolmogorov complexity to the beginning of MDL proper. The book then presents recent theoretical advances, introducing modern MDL methods in a way that is accessible to readers from many different scientific fields. The book concludes with examples of how to apply MDL in research settings that range from bioinformatics and machine learning to psychology.

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