Data perturbation for data science
44 mins 41 secs,
81.76 MB,
MP3
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About this item
| Description: |
Samworth, R
Friday 29th June 2018 - 11:00 to 11:45 |
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| Created: | 2018-06-29 17:05 |
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| Collection: | Statistical scalability |
| Publisher: | Isaac Newton Institute |
| Copyright: | Samworth, R |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | 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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