Method · Kernel and closed-form inner solvers
Provable KRR DD
Provable and Efficient Dataset Distillation for Kernel Ridge Regression
Yilan Chen, Wei Huang, Tsui-Wei Weng
NeurIPS 2024 · first public 2024-12
In one paragraph
Proves that for kernel ridge regression one data point per class is necessary and sufficient to exactly recover the original model in many settings, derives necessary and sufficient conditions for exact recovery with linear and surjective-feature-map kernels, and shows k+1 points suffice for deep linear networks with k classes; the resulting closed-form construction outperforms KIP while reported up to 15,840x faster on CIFAR-100.
Where it sits
- Kernel and closed-form inner solvers (Bi-level performance matching)
- Setting: Image classification
Builds on
BibTeX (generated; prefer the venue's official entry)
@article{chen2024provable,
title = {Provable and Efficient Dataset Distillation for Kernel Ridge Regression},
author = {Yilan Chen and Wei Huang and Tsui-Wei Weng},
journal = {NeurIPS 2024},
year = {2024}
}Nearby in Kernel and closed-form inner solvers
MMDD — Efficient Multi-modal Dataset Distillation via Analytic Parameter Matching
Deyu Bo, Xinchao Wang · ICML 2026notableVision–languagepaper ↗code ↗
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Ming-Yu Chung, Sheng-Yen Chou, Chia-Mu Yu et al. · ICLR 2024notablepaper ↗
KRR-ST — Self-Supervised Dataset Distillation for Transfer Learning
Dong Bok Lee, Seanie Lee, Joonho Ko et al. · ICLR 2024corePre-training & transferpaper ↗code ↗