Altamirano, M;
Briol, FX;
Knoblauch, J;
(2023)
Robust and Scalable Bayesian Online Changepoint Detection.
In:
Proceedings of the 40th International Conference on Machine Learning.
(pp. pp. 642-663).
PMLR: Honolulu, Hawaii, USA.
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Abstract
This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection. The resulting algorithm has key advantages over previous work: it provides provable robustness by leveraging the generalised Bayesian perspective, and also addresses the scalability issues of previous attempts. Specifically, the proposed generalised Bayesian formalism leads to conjugate posteriors whose parameters are available in closed form by leveraging diffusion score matching. The resulting algorithm is exact, can be updated through simple algebra, and is more than 10 times faster than its closest competitor.
| Type: | Proceedings paper |
|---|---|
| Title: | Robust and Scalable Bayesian Online Changepoint Detection |
| Event: | 40th International Conference on Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v202/altamirano23a.h... |
| Language: | English |
| Additional information: | Copyright 2023 by the author(s). 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/10180573 |
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