Conference item
Positively weighted kernel quadrature via subsampling
- Abstract:
- We study kernel quadrature rules with convex weights. Our approach combines the spectral properties of the kernel with recombination results about point measures. This results in effective algorithms that construct convex quadrature rules using only access to i.i.d. samples from the underlying measure and evaluation of the kernel and that result in a small worst-case error. In addition to our theoretical results and the benefits resulting from convex weights, our experiments indicate that this construction can compete with the optimal bounds in well-known examples.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Authors
- Publisher:
- Curran Associates
- Host title:
- Advances in Neural Information Processing Systems 35
- Volume:
- 10
- Pages:
- 6886-6900
- Publication date:
- 2023-04-01
- Acceptance date:
- 2022-09-14
- Event title:
- 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
- Event location:
- New Orleans, USA
- Event website:
- https://nips.cc/Conferences/2022
- Event start date:
- 2022-11-28
- Event end date:
- 2022-12-09
- ISSN:
-
1049-5258
- EISBN:
- 9781713873129
- ISBN:
- 9781713871088
- Language:
-
English
- Keywords:
- Pubs id:
-
1187579
- Local pid:
-
pubs:1187579
- Deposit date:
-
2022-11-03
Terms of use
- Copyright holder:
- Hayakawa et al.
- Copyright date:
- 2022
- Rights statement:
- © (2022) by individual authors and Neural Information Processing Systems Foundation Inc. All rights reserved.
- Notes:
- This is the accepted manuscript version of the paper. The final version is available from the Neural Information Processing Systems Foundation at: https://proceedings.neurips.cc/paper_files/paper/2022/hash/2dae7d1ccf1edf76f8ce7c282bdf4730-Abstract-Conference.html
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