Hirt, M;
Dellaportas, P;
Durmus, A;
(2019)
Copula-like variational inference.
In:
Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
33rd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada.
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Abstract
This paper considers a new family of variational distributions motivated by Sklar's theorem. This family is based on new copula-like densities on the hypercube with non-uniform marginals which can be sampled efficiently, i.e. with a complexity linear in the dimension d of the state space. Then, the proposed variational densities that we suggest can be seen as arising from these copula-like densities used as base distributions on the hypercube with Gaussian quantile functions and sparse rotation matrices as normalizing flows. The latter correspond to a rotation of the marginals with complexity O(d log d). We provide some empirical evidence that such a variational family can also approximate non-Gaussian posteriors and can be beneficial compared to Gaussian approximations. Our method performs largely comparably to state-of-the-art variational approximations on standard regression and classification benchmarks for Bayesian Neural Networks.
Type: | Proceedings paper |
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Title: | Copula-like variational inference |
Event: | 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) |
Location: | Vancouver, Canada |
Dates: | 8th-14th September 2019 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://papers.nips.cc/paper/8561-copula-like-vari... |
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 Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10081210 |
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