University of Cambridge > > Isaac Newton Institute Seminar Series > Data perturbation for data science

Data perturbation for data science

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact INI IT.

STSW04 - Future challenges in statistical scalability

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity