Falk, JIT;
Ciliberto, C;
Pontil, M;
(2022)
Implicit Kernel Meta-Learning Using Kernel Integral Forms.
In:
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022.
(pp. pp. 652-662).
PMLR 180
Preview |
Text
falk22a.pdf Download (412kB) | Preview |
Abstract
Meta-learning algorithms have made significant progress in the context of meta-learning for image classification but less attention has been given to the regression setting. In this paper we propose to learn the probability distribution representing a random feature kernel that we wish to use within kernel ridge regression (KRR). We introduce two instances of this meta-learning framework, learning a neural network pushforward for a translation-invariant kernel and an affine pushforward for a neural network random feature kernel, both mapping from a Gaussian latent distribution. We learn the parameters of the pushforward by minimizing a meta-loss associated to the KRR objective. Since the resulting kernel does not admit an analytical form, we adopt a random feature sampling approach to approximate it. We call the resulting method Implicit Kernel Meta-Learning (IKML). We derive a meta-learning bound for IKML, which shows the role played by the number of tasks T, the task sample size n, and the number of random features M. In particular the bound implies that M can be the chosen independently of T and only mildly dependent on n. We introduce one synthetic and two real-world meta-learning regression benchmark datasets. Experiments on these datasets show that IKML performs best or close to best when compared against competitive meta-learning methods.
| Type: | Proceedings paper |
|---|---|
| Title: | Implicit Kernel Meta-Learning Using Kernel Integral Forms |
| Event: | 8th Conference on Uncertainty in Artificial Intelligence, UAI 2022 |
| ISBN-13: | 9781713863298 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v180/falk22a.html |
| Language: | English |
| Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10164232 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
