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Dependency Modelling Toolbox

Dependency Modelling Toolbox

This video was recorded at 27th International Conference on Machine Learning (ICML), Haifa 2010. Investigation of dependencies between multiple data sources allows the discovery of regularities and interactions that are not seen in individual data sets. The increasing availability of co-occurring measurement data in computational biology, social sciences, and in other domains emphasizes the need for practical implementations of general-purpose dependency modeling algorithms. The project collects various dependency modeling approaches into a unified toolbox. The techniques for the discovery and analysis of statistical dependencies are based on well-established models such as probabilistic canonical correlation analysis and multi-task learning whose applicability has been demonstrated in previous case studies.


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