Luo, R;
Wang, J;
Yang, Y;
Zhu, Z;
Wang, J;
(2018)
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Advances In Neural Information Processing Systems 31 (Nips 2018).
Neural Information Processing Systems Foundation, Inc.: Montreal, Canada.
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Abstract
We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions. It simulates the Nosé-Hoover dynamics of a continuously-tempered Hamiltonian system built on the distribution of interest. A significant advantage of this method is that it is not only able to efficiently draw representative i.i.d. samples when the distribution contains multiple isolated modes, but capable of adaptively neutralising the noise arising from mini-batches and maintaining accurate sampling. While the properties of this method have been studied using synthetic distributions, experiments on three real datasets also demonstrated the gain of performance over several strong baselines with various types of neural networks plunged in.
| Type: | Proceedings paper |
|---|---|
| Title: | Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning |
| Event: | 32nd Conference on Neural Information Processing Systems (NIPS) |
| Location: | Montreal, CANADA |
| Dates: | 02 December 2018 - 08 December 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://papers.nips.cc/paper/8266-thermostat-assis... |
| 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/10079536 |
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