This is an up-to-date introduction to and overview of the Minimum Description Length (MDL)
Principle, a theory of inductive inference that can be applied to general problems in …

The recent success of high-dimensional models, such as deep neural networks (DNNs), has
led many to question the validity of the bias-variance tradeoff principle in high dimensions …

Complexity is a fundamental concept underlying statistical learning theory that aims to
inform generalization performance. Parameter count, while successful in low-dimensional …

This study focuses on detecting dependency changes in multivariate time series. This is a
practically important issue because, for example, changes in the relationships between …

When considering a data set it is often unknown how complex it is, and hence it is difficult to
assess how rich a model for the data should be. Often these choices are swept under the …

In this chapter, we introduce some methodologies for parameter estimation on the basis of
the MDL principle. Parameter estimation is the most fundamental task of statistical inference …

High-dimensional models that have hundreds of thousands of parameters such as deep
neural networks and sparse models are effective in machine learning and data mining tasks …