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Data perturbation for data science

Presented by: 
Richard Samworth University of Cambridge
Friday 29th June 2018 - 11:00 to 11:45
INI Seminar Room 1
When faced with a dataset and a problem of interest, should we propose a statistical model and use that to inform an appropriate algorithm, or dream up a potential algorithm and then seek to justify it? The former is the more traditional statistical approach, but the latter appears to be becoming more popular. I will discuss a class of algorithms that belong in the second category, namely those that involve data perturbation (e.g. subsampling, random projections, artificial noise, knockoffs,...). As examples, I will consider Complementary Pairs Stability Selection for variable selection and sparse PCA via random projections. This will involve joint work with Rajen Shah, Milana Gataric and Tengyao Wang.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons