On the Size and Approximation Error of Distilled Sets
Alaa Maalouf Murad Tukan Noel Loo Ramin Hasani Mathias Lechner Daniela Rus
Dataset Distillation is the task of synthesizing small datasets from large
ones while still retaining comparable predictive accuracy to the original
uncompressed dataset. Despite significant empirical progress in recent years,
there is little understanding of the theoretical limitations/guarantees of
dataset distillation, specifically, what excess risk is achieved by
distillation compared to the original dataset, and how large are distilled
datasets? In this work, we take a theoretical view on kernel ridge regression
(KRR) based methods of dataset distillation such as Kernel Inducing Points. By
transforming ridge regression in random Fourier features (RFF) space, we
provide the first proof of the existence of small (size) distilled datasets and
their corresponding excess risk for shift-invariant kernels. We prove that a
small set of instances exists in the original input space such that its
solution in the RFF space coincides with the solution of the original data. We
further show that a KRR solution can be generated using this distilled set of
instances which gives an approximation towards the KRR solution optimized on
the full input data. The size of this set is linear in the dimension of the RFF
space of the input set or alternatively near linear in the number of effective
degrees of freedom, which is a function of the kernel, number of datapoints,
and the regularization parameter $\lambda$. The error bound of this distilled
set is also a function of $\lambda$. We verify our bounds analytically and
empirically.