Botev, A;
Zheng, B;
Barber, D;
(2017)
Complementary sum sampling for likelihood approximation in large scale classification.
In: Singh, Aarti and Zhu, Jerry, (eds.)
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017).
PMLR (Proceedings of Machine Learning Research): Fort Lauderdale, FL, USA.
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Abstract
We consider training probabilistic classifiers in the case that the number of classes is too large to perform exact normalisation over all classes. We show that the source of high variance in standard sampling approximations is due to simply not including the correct class of the datapoint into the approximation. To account for this we explicitly sum over a subset of classes and sample the remaining. We show that this simple approach is competitive with recently introduced non likelihood-based approximations.
| Type: | Proceedings paper |
|---|---|
| Title: | Complementary sum sampling for likelihood approximation in large scale classification |
| Event: | 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017), 20-22 April 2017, Fort Lauderdale, FL, USA |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://proceedings.mlr.press/v54/ |
| 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 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.ucl.ac.uk/id/eprint/10079524 |
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