Variational inference for sparse gaussian process modulated hawkes process

Zhang, R; Walder, C; Rizoiu, MA

Variational inference for sparse gaussian process modulated hawkes process

Zhang, R Walder, C Rizoiu, MA

Permalink

Publisher:: AAAI
Publication Type:: Conference Proceeding
Citation:: AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 2020, 34, (4), pp. 6803-6810
Issue Date:: 2020-01-01

Closed Access

	Filename	Description	Size
	6160-Article Text-9385-1-10-20200513.pdf	Published version	773.89 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Zhang, R
dc.contributor.author	Walder, C
dc.contributor.author	Rizoiu, MA
dc.date	2020-02-07
dc.date.accessioned	2021-06-20T21:26:42Z
dc.date.available	2021-06-20T21:26:42Z
dc.date.issued	2020-01-01
dc.identifier.citation	AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 2020, 34, (4), pp. 6803-6810
dc.identifier.isbn	9781577358350
dc.identifier.uri	http://hdl.handle.net/10453/149650
dc.description.abstract	The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the nonparametric Bayesian HP has been proposed. However, the inference of such models suffers from unscalability or slow convergence. In this paper, we aim to solve both problems. Specifically, first, we propose a new non-parametric Bayesian HP in which the triggering kernel is modeled as a squared sparse Gaussian process. Then, we propose a novel variational inference schema for model optimization. We employ the branching structure of the HP so that maximization of evidence lower bound (ELBO) is tractable by the expectation-maximization algorithm. We propose a tighter ELBO which improves the fitting performance. Further, we accelerate the novel variational inference schema to linear time complexity by leveraging the stationarity of the triggering kernel. Different from prior acceleration methods, ours enjoys higher efficiency. Finally, we exploit synthetic data and two large social media datasets to evaluate our method. We show that our approach outperforms state-of-the-art non-parametric frequentist and Bayesian methods. We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection. We observe that the tighter ELBO exceeds the common one in model selection.
dc.language	en
dc.publisher	AAAI
dc.relation.ispartof	AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
dc.relation.ispartof	AAAI Conference on Artificial Intelligence
dc.relation.isbasedon	10.1609/aaai.v34i04.6160
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Variational inference for sparse gaussian process modulated hawkes process
dc.type	Conference Proceeding
utslib.citation.volume	34
utslib.location.activity	New York, New York, USA
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-06-20T21:26:41Z
pubs.finish-date	2020-02-12
pubs.issue	4
pubs.place-of-publication	USA
pubs.publication-status	Published
pubs.start-date	2020-02-07
pubs.volume	34
utslib.citation.issue	4
dc.location	USA

Abstract:

The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the nonparametric Bayesian HP has been proposed. However, the inference of such models suffers from unscalability or slow convergence. In this paper, we aim to solve both problems. Specifically, first, we propose a new non-parametric Bayesian HP in which the triggering kernel is modeled as a squared sparse Gaussian process. Then, we propose a novel variational inference schema for model optimization. We employ the branching structure of the HP so that maximization of evidence lower bound (ELBO) is tractable by the expectation-maximization algorithm. We propose a tighter ELBO which improves the fitting performance. Further, we accelerate the novel variational inference schema to linear time complexity by leveraging the stationarity of the triggering kernel. Different from prior acceleration methods, ours enjoys higher efficiency. Finally, we exploit synthetic data and two large social media datasets to evaluate our method. We show that our approach outperforms state-of-the-art non-parametric frequentist and Bayesian methods. We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection. We observe that the tighter ELBO exceeds the common one in model selection.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/149650