Publications
Authors:
  • Guillaume Bouchard , Onno Zoeter
Citation:
ICML International Conference on Machine Learning, Montreal, Quebec, June 14-17, 2009
Abstract:
We propose a deterministic method to evaluate the integral of a positive function based on soft-binning functions that smoothly cut the integral into smaller integrals that are easier to approximate. In combination with mean-field approximations for each individual sub-part this leads to a tractable algorithm that alternates between the optimization of the bins and the approximation of the local integrals. We introduce suitable choices
for the binning functions such that a standard mean field approximation can be extended to a split mean field approximation without the need for extra derivations. The method can be seen as a revival of the ideas underlying the mixture mean field approach. The latter can be obtained as a special case
by taking soft-max functions for the binning.
Year:
2009
Report number:
2009/010