Methods › General › Meta-Learning Algorithms › KIP
Kernel Inducing Points
KIP
Introduced by Timothy Nguyen et al. in Dataset Meta-Learning from Kernel Ridge-Regression
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Kernel Inducing Points, or KIP, is a meta-learning algorithm for learning datasets that can mitigate the challenges which occur for naturally occurring datasets without a significant sacrifice in performance. KIP uses kernel-ridge regression to learn ϵ-approximate datasets. It can be regarded as an adaption of the inducing point method for Gaussian processes to the case of Kernel Ridge Regression.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Efficient Dataset Distillation Using Random Feature Approximation 21 Oct 2022 · 2 repositories · arXiv:2210.12067Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
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Dataset Meta-Learning from Kernel-Ridge Regression 1 Jan 2021 · 0 repositories
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Dataset Meta-Learning from Kernel Ridge-Regression 30 Oct 2020 · 1 repository · arXiv:2011.00050
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Dataset Distillation | 3 |
| regression | 3 |
| Meta-Learning | 2 |
| Dataset Condensation | 1 |
| GPU | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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