Conference item icon

Conference item

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...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
ORCID:
0000-0001-5547-9213
Publisher:
Proceedings of Machine Learning Research Publisher's website
Journal:
21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018) Journal website
Host title:
21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018)
Publication date:
2018-03-31
Acceptance date:
2017-12-22
Source identifiers:
813212
Pubs id:
pubs:813212
UUID:
uuid:a9360daf-a537-4ea3-97ca-fe866e0f2da0
Local pid:
pubs:813212
Deposit date:
2017-12-29

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP