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Bayesian approaches to distribution regression

Abstract:

Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level. Current approaches, however, do not propagate the uncertainty in observations due to sampling variability in the groups. This effectively assumes that small and large groups are estimated equally well, and should have equal weight in the final regression. We account for this uncertainty with...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Role:
Author
ORCID:
0000-0001-5547-9213
Publisher:
Proceedings of Machine Learning Research Publisher's website
Publication date:
2018-03-31
Acceptance date:
2017-12-22
Pubs id:
pubs:813212
URN:
uri:a9360daf-a537-4ea3-97ca-fe866e0f2da0
UUID:
uuid:a9360daf-a537-4ea3-97ca-fe866e0f2da0
Local pid:
pubs:813212

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