Maurer, A;
Pontil, M;
(2018)
Empirical bounds for functions with weak interactions.
In: Bubeck, S and Perchet, V and Rigollet, P, (eds.)
Proceedings of the 31st Annual Conference on Learning Theory (COLT 2018).
(pp. pp. 987-1010).
PMLR (Proceedings of Machine Learning Research): Stockholm.
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Abstract
We provide sharp empirical estimates of expectation, variance and normal approximation for a class of statistics whose variation in any argument does not change too much when another argument is modified. Examples of such weak interactions are furnished by U- and V-statistics, Lipschitz Lstatistics and various error functionals of `2-regularized algorithms and Gibbs algorithms.
| Type: | Proceedings paper |
|---|---|
| Title: | Empirical bounds for functions with weak interactions |
| Event: | 31st Annual Conference on Learning Theory, 6-9 July 2018, Stockholm, Sweden |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://proceedings.mlr.press/v75/ |
| 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 UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS 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 |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10073435 |
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