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Complementary sum sampling for likelihood approximation in large scale classification

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. Green open access

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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 > 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: http://discovery.ucl.ac.uk/id/eprint/10079524
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