Maurer, A;
Pontil, M;
(2021)
Concentration inequalities under sub-Gaussian and sub-exponential conditions.
In: Ranzato, M and Beygelzimer, A and Dauphin, Y and Liang, PS and Wortman Vaughan, J, (eds.)
Advances in Neural Information Processing Systems.
(pp. pp. 7588-7597).
NeurIPS
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Abstract
We prove analogues of the popular bounded difference inequality (also called McDiarmid’s inequality) for functions of independent random variables under sub-Gaussian and sub-exponential conditions. Applied to vector-valued concentration and the method of Rademacher complexities these inequalities allow an easy extension of uniform convergence results for PCA and linear regression to the case of potentially unbounded input- and output variables.
| Type: | Proceedings paper |
|---|---|
| Title: | Concentration inequalities under sub-Gaussian and sub-exponential conditions |
| Event: | 35th Conference on Neural Information Processing Systems (NeurIPS 2021) |
| ISBN-13: | 9781713845393 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.neurips.cc/paper/2021/hash/3e3... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10150640 |
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