Friday, June 21, 2019 - 11:10am - 12:00pm
Steven Wu (University of Minnesota, Twin Cities)
We present a general method for privacy-preserving Bayesian inference in Poisson factorization, a broad class of models that includes some of the most widely used models in the social sciences. Our method satisfies limited precision local privacy, a generalization of local differential privacy, which we introduce to formulate privacy guarantees appropriate for sparse count data. We develop an MCMC algorithm that approximates the locally private posterior over model parameters given data that has been locally privatized by the geometric mechanism (Ghosh et al., 2012).
Tuesday, April 24, 2018 - 11:00am - 11:30am
Peter Filzmoser (Technische Universität Wien)
The maximum association between two multivariate variables X and Y is defined as the maximal value that a bivariate ssociation measure between one-dimensional projections a'X and b'Y can attain. Taking the Pearson correlation as projection index results in the first canonical correlation coefficient. We propose to use more robust association measures, such as Spearman's or Kendall's rank correlation, or association measures derived from bivariate scatter matrices.
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