Conference item
Bayesian learning from sequential data using Gaussian processes with signature covariances
- Abstract:
-
We develop a Bayesian approach to learning from sequential data by using Gaussian processes (GPs) with so-called signature kernels as covariance functions. This allows to make sequences of different length comparable and to rely on strong theoretical results from stochastic analysis. Signatures capture sequential structure with tensors that can scale unfavourably in sequence length and state space dimension. To deal with this, we introduce a sparse variational approach with inducing tensors. ...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, 3.1MB, Terms of use)
-
- Publication website:
- http://proceedings.mlr.press/v119/toth20a.html
Authors
Bibliographic Details
- Publisher:
- Proceedings of Machine Learning Research
- Journal:
- Proceedings of Machine Learning Research More from this journal
- Volume:
- 119
- Pages:
- 9548-9560
- Publication date:
- 2020-11-21
- Acceptance date:
- 2020-06-01
- Event title:
- 37th International Conference of Machine Learning (ICML 2020)
- Event location:
- Vienna, Austria
- Event website:
- https://icml.cc/Conferences/2020
- Event start date:
- 2020-07-12
- Event end date:
- 2020-07-18
Item Description
- Language:
-
English
- Keywords:
- Pubs id:
-
1118482
- Local pid:
-
pubs:1118482
- Deposit date:
-
2020-07-13
Terms of use
- Copyright holder:
- Toth and Oberhauser
- Copyright date:
- 2020
- Rights statement:
- © The Author(s) 2020. Open Access under a Creative Commons Attribution licence.
- Notes:
- This paper was presented at the 37th International Conference on Machine Learning (ICML 2020), July 2020.
Metrics
If you are the owner of this record, you can report an update to it here: Report update to this record