Quasi-Random Resamplings, with Applications to Rule Extraction, Cross-Validation and (Su-)Bagging
author:
Olivier Teytaud,
INRIA
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| Slides | |
| 0:00 | Quasi-random resampling |
| 0:17 | What is the problem? |
| 1:09 | A typical example |
| 2:34 | Organization of the talk |
| 2:39 | Why resampling is Monte-Carlo integration |
| 4:30 | An introduction to QR-numbers |
| 4:53 | QR-numbers |
| 5:31 | Quasi-random numbers? |
| 6:39 | Quasi-random = low discrepancy? |
| 7:48 | A better discrepancy? |
| 8:19 | Existing bounds on low-discrepancy-Monte-Carlo |
| 9:00 | Which set do you trust? |
| 9:45 | Which quasi-random numbers? |
| 10:21 | What else than Monte-Carlo integration? |
| 10:49 | ... and how to do in strange spaces? |
| 11:01 | Have fun with QR in strange spaces |
| 11:31 | Quasi-random numbers in strange spaces |
| 11:35 | ... but we need something else! |
| 11:57 | Quasi-random points in strange spaces |
| 12:05 | Why not in something isotropic? |
| 12:11 | For the gaussian: easy! |
| 13:22 | Ok! |
| 14:19 | However, we will do that |
| 14:53 | QR-numbers in resampling pt 1 |
| 15:18 | QR-numbers in resampling pt 2 |
| 16:16 | QR-numbers in resampling pt 3 |
| 16:24 | What are bootstrap samples? |
| 16:28 | A naive solution pt 1 |
| 17:38 | A naive solution pt 2 |
| 17:47 | A naive solution pt 3 |
| 17:48 | A naive solution pt 4 |
| 17:52 | A naive solution pt 5 |
| 18:15 | A naive solution pt 6 |
| 18:58 | ...which does not work |
| 19:08 | A less naive solution |
| 20:13 | With dimension-reduction it's better |
| 21:07 | Summary |
| 21:11 | Conclusions pt 1 |
| 21:14 | Experiments |
| 24:22 | A typical example |
| 25:42 | Conclusions pt 2 |
| 26:01 | Further work |
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