eprintid: 10180942
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/18/09/42
datestamp: 2023-11-10 13:58:33
lastmod: 2023-11-10 13:58:33
status_changed: 2023-11-10 13:58:33
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Sellier, J
creators_name: Dellaportas, P
title: Bayesian online change point detection with Hilbert space approximate Student-t process
ispublished: pub
divisions: UCL
divisions: B04
divisions: C06
divisions: F61
note: © The Authors 2023. Original content in this paper is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).
abstract: In this paper, we introduce a variant of Bayesian online change point detection with a reduced-rank Student-t process (TP) and dependent Student-t noise, as a nonparametric time series model. Our method builds and improves upon the state-of-the-art Gaussian process (GP) change point model benchmark of Saatçi et al. (2010). The Student-t process generalizes the concept of a GP and hence yields a more flexible alternative. Additionally, unlike a GP, the predictive variance explicitly depends on the training observations, while the use of an entangled Student-t noise model preserves analytical tractability. Our approach also uses a Hilbert space reduced-rank representation of the TP kernel, derived from an eigenfunction expansion of the Laplace operator (Solin & Särkkä, 2020), to alleviate its computational complexity. Improvements in prediction and training time are demonstrated with real-world data sets.
date: 2023
date_type: published
publisher: PMLR (Proceedings of Machine Learning Research)
official_url: https://proceedings.mlr.press/v202/sellier23a.html
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2106060
lyricists_name: Dellaportas, Petros
lyricists_id: PDELL40
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
pres_type: paper
publication: Proceedings of Machine Learning Research
volume: 202
pagerange: 30553-30569
issn: 2640-3498
book_title: Proceedings of the 40th International Conference on Machine Learning
citation:        Sellier, J;    Dellaportas, P;      (2023)    Bayesian online change point detection with Hilbert space approximate Student-t process.                     In:  Proceedings of the 40th International Conference on Machine Learning.  (pp. pp. 30553-30569).  PMLR (Proceedings of Machine Learning Research)       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10180942/1/sellier23a.pdf