Recent success in deep learning has partially been driven by training increasingly
overparametrized networks on ever larger datasets. It is therefore natural to ask: how much …

Coreset selection, which aims to select a subset of the most informative training samples, is
a long-standing learning problem that can benefit many downstream tasks such as data …

We propose a new dataset distillation algorithm using reparameterization and
convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art …

Given a set of $ n $ points in $ d $ dimensions, the Euclidean $ k $-means problem (resp.
Euclidean $ k $-median) consists of finding $ k $ centers such that the sum of squared …

Given a metric space, the (k, z)-clustering problem consists of finding k centers such that the
sum of the of distances raised to the power z of every point to its closest center is minimized …

The (k, z)-clustering problem consists of finding a set of k points called centers, such that the
sum of distances raised to the power of z of every data point to its closest center is …

Motivated by practical generalizations of the classic k-median and k-means objectives, such
as clustering with size constraints, fair clustering, and Wasserstein barycenter, we introduce …

A coreset for a set of points is a small subset of weighted points that approximately
preserves important properties of the original set. Specifically, if $ P $ is a set of points, $ Q …

In large scale machine learning, random sampling is a popular way to approximate datasets
by a small representative subset of examples. In particular, sensitivity sampling is an …

We present a novel global compression framework for deep neural networks that
automatically analyzes each layer to identify the optimal per-layer compression ratio, while …