Analysis & theory
A Theoretical Study of Dataset Distillation
Zachary Izzo, James Zou
NeurIPS 2023 Workshop · first public 2023-12
In one paragraph
Proves existence and impossibility results for exact dataset distillation of generalized linear models: a single synthetic point can exactly reproduce a model trained on the full data for GLMs, and a size-independent construction exists for linear regression with any data-independent regularizer, but no analogous construction exists for logistic regression, and kernel regression cannot in general be distilled to recover even one model exactly.
Where it sits
- Setting: Image classification
BibTeX (generated; prefer the venue's official entry)
@article{izzo2023theoretical,
title = {A Theoretical Study of Dataset Distillation},
author = {Zachary Izzo and James Zou},
journal = {NeurIPS 2023 Workshop},
year = {2023}
}