The Library
Distribution regression for sequential data
Tools
Tools
Tools
Lemercier, Maud, Salvi, Cristopher, Damoulas, Theodoros, Bonilla, Edwin V. and Lyons, Terry (2021) Distribution regression for sequential data. In: The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), Virtual, 13-15 Apr 2021. Published in: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 130 pp. 3754-3762. ISSN 2640-3498.
|
PDF
WRAP-distribution-regression-sequential%20data-Damoulas-2021.pdf - Published Version - Requires a PDF viewer. Download (3076Kb) | Preview |
|
![[img]](https://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-distribution-regression-sequential data-Damoulas-2021.pdf - Submitted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (4Mb) |
Official URL: https://proceedings.mlr.press/v130/lemercier21a.ht...
Abstract
Distribution regression refers to the supervised learning problem where labels are only available for groups of inputs instead of individual inputs. In this paper, we develop a rigorous mathematical framework for distribution regression where inputs are complex data streams. Leveraging properties of the expected signature and a recent signature kernel trick for sequential data from stochastic analysis, we introduce two new learning techniques, one feature-based and the other kernel-based. Each is suited to a different data regime in terms of the number of data streams and the dimensionality of the individual streams. We provide theoretical results on the universality of both approaches and demonstrate empirically their robustness to irregularly sampled multivariate time-series, achieving state-of-the-art performance on both synthetic and real-world examples from thermodynamics, mathematical finance and agricultural science.
| Item Type: | Conference Item (Paper) | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Statistics |
||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Machine learning, Statistical decision, Artificial intelligence, Mathematical statistics, Electronic data processing -- Distributed processing -- Statistical methods, Regression analysis | ||||||||||||||||||
| Series Name: | Proceedings of Machine Learning Research | ||||||||||||||||||
| Journal or Publication Title: | Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) | ||||||||||||||||||
| Publisher: | PMLR | ||||||||||||||||||
| ISSN: | 2640-3498 | ||||||||||||||||||
| Official Date: | 2021 | ||||||||||||||||||
| Dates: |
|
||||||||||||||||||
| Volume: | 130 | ||||||||||||||||||
| Page Range: | pp. 3754-3762 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||
| Date of first compliant deposit: | 2 September 2021 | ||||||||||||||||||
| Date of first compliant Open Access: | 2 September 2021 | ||||||||||||||||||
| Funder: | |||||||||||||||||||
| RIOXX Funder/Project Grant: |
|
||||||||||||||||||
| Conference Paper Type: | Paper | ||||||||||||||||||
| Title of Event: | The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021) | ||||||||||||||||||
| Type of Event: | Conference | ||||||||||||||||||
| Location of Event: | Virtual | ||||||||||||||||||
| Date(s) of Event: | 13-15 Apr 2021 | ||||||||||||||||||
| Related URLs: | |||||||||||||||||||
| Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
