During the last few years (2004-2007), there have been several breakthroughs in the area of Minimum
Description Length (MDL) modeling, learning and prediction. These breakthroughs concern the efficient computation and proper formulation of MDL in parametric problems based on the “normalized maximum likelihood”, as well as altogether new, and better, coding schemes for nonparametric problems.
This essentially solves the so-called AIC-BIC dilemma, which has been a central problem in statistical model selection for more than 20 years now. The goal of this workshop is to introduce these exciting new developments to the ML and UAI communities, and to foster new collaborations between interested researchers.
Most new developments that are the focus of this workshop concern efficient (in many cases, linear- time) algorithms for theoretically optimal inference procedures that were previously thought not to be efficiently solvable. It is therefore hoped that the workshop will inspire original practical applications of MDL in machine learning domains.
Development of such applications recently became a lot easier, because of the new (2007) book on MDL by P. Grunwald, which provides the first comprehensive overview of the field, as well as in-depth discussions of how it relates to other approaches such as Bayesian inference. Remarkably, the originator of MDL, J. Rissanen, also published a new monograph in 2007;
and a Festschrift in Honor of Rissanen’s 75th birthday was presented to him in May 2008.