Dagstuhl-Seminar — Statistical and Probabilistic Methods in Algorithmic Data Analysis

Modern algorithms for data analysis require the use of advanced probabilistic methods to achieve desirable scalability and accuracy guarantees. At the same time, modern data-analysis tasks require the use of advanced statistics to handle challenges such as testing for multiple hypotheses or identifying dependencies among data points, such as in time series or graphs. Probabilistic methods are also at the core theoretical computer-science areas, such as sublinear algorithms and average-case analysis. To obtain efficient data-analysis algorithms, probabilistic methods require careful balancing of theoretical and practical aspects. This Dagstuhl Seminar brings together researchers interested in statistical and probabilistic methods to design and analyze scalable algorithms for discovering knowledge in large and rich datasets. We plan to cover the following topics, among others.