eprintid: 10079536
rev_number: 20
eprint_status: archive
userid: 608
dir: disk0/10/07/95/36
datestamp: 2019-08-09 14:57:29
lastmod: 2021-10-04 00:26:52
status_changed: 2019-08-14 14:19:37
type: proceedings_section
metadata_visibility: show
creators_name: Luo, R
creators_name: Wang, J
creators_name: Yang, Y
creators_name: Zhu, Z
creators_name: Wang, J
title: Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2018-12-08
date_type: published
publisher: Neural Information Processing Systems Foundation, Inc.
official_url: https://papers.nips.cc/paper/8266-thermostat-assisted-continuously-tempered-hamiltonian-monte-carlo-for-bayesian-learning
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1647409
lyricists_name: Wang, Jun
lyricists_id: JWANG00
actors_name: Wang, Jun
actors_id: JWANG00
actors_role: owner
full_text_status: public
series: Advances in Neural Information Processing Systems
publication: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018)
volume: 31
place_of_pub: Montreal, Canada
pages: 10
event_title: 32nd Conference on Neural Information Processing Systems (NIPS)
event_location: Montreal, CANADA
event_dates: 02 December 2018 - 08 December 2018
institution: 32nd Conference on Neural Information Processing Systems (NIPS)
issn: 1049-5258
book_title: Advances In Neural Information Processing Systems 31 (Nips 2018)
editors_name: Bengio, S
editors_name: Wallach, H
editors_name: Larochelle, H
editors_name: Grauman, K
editors_name: CesaBianchi, N
editors_name: Garnett, R
citation:        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.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10079536/1/8266-thermostat-assisted-continuously-tempered-hamiltonian-monte-carlo-for-bayesian-learning.pdf