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Distribution regression for sequential data

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. Eac...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publication website:
https://proceedings.mlr.press/v130/lemercier21a.html

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Oxford college:
St Anne's College
Role:
Author
ORCID:
0000-0002-9972-2809
Publisher:
Journal of Machine Learning Research Publisher's website
Pages:
3754-3762
Series:
Proceedings of Machine Learning Research
Series number:
130
Host title:
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics
Publication date:
2021-03-18
Acceptance date:
2021-01-23
Event title:
24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)
Event location:
Virtual event
Event website:
https://aistats.org/aistats2021/
Event start date:
2021-04-13T00:00:00Z
Event end date:
2021-04-15T00:00:00Z
ISSN:
2640-3498
Language:
English
Keywords:
Pubs id:
1119383
Local pid:
pubs:1119383
Deposit date:
2021-12-14

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